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54 lines
1.5 KiB
Plaintext
54 lines
1.5 KiB
Plaintext
//
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// Check input samplesheet and get read channels
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//
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include { DATABASE_CHECK } from '../../modules/local/database_check'
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include { UNTAR } from '../../modules/nf-core/untar/main'
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workflow DB_CHECK {
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take:
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dbsheet // file: /path/to/dbsheet.csv
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main:
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// TODO: make database sheet check
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// Checks:
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// 1) no duplicates,
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// 2) args do not have quotes, e.g. just `,,` and NOT `,"",`
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parsed_samplesheet = DATABASE_CHECK ( dbsheet )
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.csv
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.splitCsv ( header:true, sep:',' )
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.map { create_db_channels(it) }
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ch_dbs_for_untar = parsed_samplesheet
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.branch {
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untar: it[1].toString().endsWith(".tar.gz")
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skip: true
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}
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// TODO Filter to only run UNTAR on DBs of tools actually using?
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// TODO make optional whether to save
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UNTAR ( ch_dbs_for_untar.untar )
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ch_final_dbs = ch_dbs_for_untar.skip.mix( UNTAR.out.untar )
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emit:
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dbs = ch_final_dbs // channel: [ val(meta), [ db ] ]
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versions = DATABASE_CHECK.out.versions.mix(UNTAR.out.versions.first()) // channel: [ versions.yml ]
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}
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def create_db_channels(LinkedHashMap row) {
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def meta = [:]
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meta.tool = row.tool
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meta.db_name = row.db_name
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meta.db_params = row.db_params
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def array = []
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if (!file(row.db_path, type: 'dir').exists()) {
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exit 1, "ERROR: Please check input samplesheet -> database could not be found!\n${row.db_path}"
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}
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array = [ meta, file(row.db_path) ]
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return array
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}
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