nextflow.enable.dsl=2

// params.container_biotransformer = 'harbor.cluster.omic.ai/omic/digitaltrials/biotransformer:latest'
// params.container_conplex = 'harbor.cluster.omic.ai/omic/digitaltrials/conplex_dig_pat:latest'
// params.container_tissue = 'harbor.cluster.omic.ai/omic/digitaltrials/tissue:latest'
// params.container_preprocess = 'harbor.cluster.omic.ai/omic/metabolite-screen:project'
// params.container_mass_screen = 'harbor.cluster.omic.ai/omic/metabolite-screen:adaptive' 
// params.container_chembl = 'harbor.cluster.omic.ai/omic/digitaltrials/chembl:1.0.0' 

params.container_biotransformer = 'harbor.cluster.omic.ai/omic/digitaltrials/biotransformer@sha256:fb8bdc0b65376bc154b6051ae07079dd9a0e25c3f4c02de73c502002a94d69d5'
params.container_conplex = 'harbor.cluster.omic.ai/omic/digitaltrials/conplex_dig_pat@sha256:7a3523dba6fa01e3adc9cb79af5e1dcbd2a19d9f92e37cd10df462766078ede3'
params.container_tissue = 'harbor.cluster.omic.ai/omic/digitaltrials/tissue:1.1.2'
params.container_preprocess = 'harbor.cluster.omic.ai/omic/metabolite-screen@sha256:872c395e21abd4afea4185b269a8218da4737bd7adbb2cbe2bdf9a1b9c70db17'
params.container_mass_screen = 'harbor.cluster.omic.ai/omic/metabolite-screen:adaptive-1.2.0'
params.container_chembl = 'harbor.cluster.omic.ai/omic/digitaltrials/chembl:1.0.0' 

params.containerOptions = '--rm' // '--gpus all --rm -v /mnt:/mnt'


//BIOTRANSFORMER
params.project_name = 'test'
//params.ligands = '/Workspace/next/registry/pipelines/digital_trials/input'

//IMPOTRANT!!! use HUMAN. Only one that returns what is expected. metabolism to the last step. It's only one properly set up
params.mode = 'HUMAN' // Options: SUPER, HUMAN, MASS, ORDERED # only HUMAN is fully implemented
params.bt_initial_memory = 5        // GB - starting memory for biotransformer
params.bt_growth_memory = 15        // GB - additional memory per retry
params.bt_max_retries = 10
params.bt_fail_action = 'ignore' // 'terminate' or 'ignore'
params.bt_max_forks = 0             // 0 = unlimited, set to N to limit concurrency

// GET_FINAL_METABOLITES_STATIC — queries the ~9.2 GB ChEMBL SQLite DB on the PVC.
// The DB is opened read-only/immutable and is NOT staged into the task workdir, so the
// memory below covers rdkit + the biotransformer CSV, not the database itself.
params.chembl_initial_memory = 4         // GB - starting memory
params.chembl_growth_memory  = 4         // GB - additional memory per retry attempt
params.chembl_max_retries    = 3
params.chembl_fail_action    = 'ignore'  // 'terminate' or 'ignore' after retries exhausted

//CONPLEX
params.keep_enst = 'false' //'true' //'false' //to keep individual protein data created by conplex step
params.conplex_initial_memory = 5   // GB - starting memory for conplex
params.conplex_growth_memory = 15   // GB - additional memory per retry
params.conplex_max_retries = 1
params.conplex_fail_action = 'ignore' // 'terminate' or 'ignore'
// params.mutated_protein_csv = '/Workspace/next/registry/pipelines/digital_trials/MANE_all_transcipts.csv'
params.threshold = 0.65 //0.65 //0.8 //0.5 //0.7 
// params.screening_batch_size =100000 //100k is ideal to optimize performance with virtual screening
params.protein_network_threshold = 0.65 //0.65 //threshold for stirng input
// NETWORK_ENRICHMENT (string-db) — Python-level urllib3 Retry handles 429/5xx blips first
// so most rate-limit failures are absorbed without spawning a Nextflow retry workdir.
params.string_max_forks      = 5         // 0 = unlimited; cap below string-db's ~10 req/s throttle
params.string_max_retries    = 1         // Nextflow-level fallback retries (kept low — Python retries are the primary defense)
params.string_fail_action    = 'ignore'  // 'terminate' or 'ignore' after max retries exhausted
params.string_initial_memory = 1         // GB - starting memory; the script reads only transcipt+conplex_score columns
params.string_growth_memory  = 2         // GB - additional memory per retry attempt

// TISSUE_DISTRIBUTION + BIO_METRICS (tissue container) — both scripts now use
// usecols= on significant_interactions.tsv so a 5+ GB input only loads the
// 'transcipt' (and 'conplex_score' for BIO_METRICS) column.
params.tissue_initial_memory = 5         // GB
params.tissue_growth_memory  = 5         // GB per retry attempt
params.tissue_max_retries    = 2
params.tissue_fail_action    = 'ignore'

params.bio_initial_memory    = 5         // GB
params.bio_growth_memory     = 5         // GB per retry attempt
params.bio_max_retries       = 2
params.bio_fail_action       = 'ignore'

//TISSUE DISTRIBUTION / BIO PROP

// ========================================= ALL RELEVANT FILEPATHS IN THIS SECTION ===================================================
// Defaults below are the PVC mount paths used for local/k8s execution. WES overrides
// ligands/outdir via experiment_params (it translates s3:// URIs to PVC mount paths).

params.outdir = '/omic/eureka/digital-trial/output'

// Ligand CSVs staged in the eureka workspace (s3://omic/eureka/digital-trial/input/ligands/)
params.ligands = '/omic/eureka/digital-trial/input/ligands'

// Intentionally empty: no *.fasta here means PREPROCESS_PROTEIN is never scheduled and
// the pipeline screens against the prebuilt protein_zarr below. Do NOT point this at a
// directory containing .fasta files — params.container_preprocess is pinned to a digest
// that no longer exists in Harbor, so the process would fail on image pull.
params.mutated_protein_fasta = '/mnt/dreamdock-data/digital_trials/input/blank'

// Prebuilt reference data on the dreamdock-data PVC (verified present on k8s-node23)
params.protein_zarr = '/mnt/dreamdock-data/digital_trials/zarr/protein_seq.zarr'   // 7.5 MB
params.chembl_db    = '/mnt/dreamdock-data/digital_trials/chembl/chembl_36.db'     // 9.2 GB

// =====================================================================================================================================

//BIOTRANSFORMER
include { SUPER_TRANSFORMER } from './main_biotransformer.nf'
include { HUMAN_TRANSFORMER } from './main_biotransformer.nf'
include { METABOLITES_BY_MASS } from './main_biotransformer.nf'
include { ORDERED_SEQUENCE } from './main_biotransformer.nf'
// include { GET_FINAL_METABOLITES } from './main_biotransformer.nf'
include { GET_FINAL_METABOLITES_STATIC } from './main_biotransformer.nf'
//CONPLEX
include { CONPLEX as CONPLEX_ALL } from './main_conplex.nf'
include {  PREPROCESS_PROTEIN } from './main_conplex.nf'

include {  MERGE_DRUG } from './main_conplex.nf'
include {  MERGE_INTERACTIONS } from './main_conplex.nf'

include { NETWORK_ENRICHMENT } from './main_conplex.nf'
//TISSUE_DISTRIBUTION / BIO_PROP
include { TISSUE_DISTRIBUTION } from './main_tissue.nf'
include { BIO_METRICS } from './main_tissue.nf'

workflow {
    //BIOTRANSFORMER
    lig_ch = Channel.fromPath("${params.ligands}/*.csv")
    switch (params.mode) {
        case 'SUPER':
            SUPER_TRANSFORMER(lig_ch)
            break
        case 'HUMAN':
            HUMAN_TRANSFORMER(lig_ch)
            break
        case 'MASS':
            METABOLITES_BY_MASS(lig_ch)
            break
        case 'ORDERED':
            ORDERED_SEQUENCE(lig_ch)
            break
        default:
            println("Invalid mode specified: ${params.mode}")
    }
    // Pass the ChEMBL DB as a plain path string rather than a staged file: at ~9.2 GB,
    // staging it per task copied the DB into every work directory. The process reads it
    // read-only from the PVC mount instead.
    chembl_ch = Channel.value(params.chembl_db)
    // GET_FINAL_METABOLITES(HUMAN_TRANSFORMER.out)    // TODO: LOCALIZE
    GET_FINAL_METABOLITES_STATIC(HUMAN_TRANSFORMER.out, chembl_ch)
    //CONPLEX

    protein_fasta = Channel
    .fromPath("${params.mutated_protein_fasta}/*.fasta")
    .collect()
    // protein_csv = file(params.mutated_protein_csv)
    protein_zarr = PREPROCESS_PROTEIN(protein_fasta).collect().map { it[0] }
    pre_protein_zarr = Channel.fromPath(params.protein_zarr)

    // Merge both zarr channels into a single list
    merged_zarr = protein_zarr.concat(pre_protein_zarr).collect()

    lig_with_id = lig_ch.map { csv -> 
        [csv.simpleName, csv] 
    }

    metabolite_with_id = GET_FINAL_METABOLITES_STATIC.out.map { txt -> 
        def name = txt.simpleName.replaceAll(/_out$/, '')
        [name, txt]
    }

    // Join on the identifier and remove it
    matched_ch = lig_with_id
        .join(metabolite_with_id)
        // .map { id, ligand, metabolite -> [ligand, metabolite] }

    // DEBUG_MATCH(matched_ch, merged_zarr)

    CONPLEX_ALL(matched_ch, merged_zarr)

    conplex_drug_ch = CONPLEX_ALL.out.map { id, drug, interactions -> drug }
    conplex_interactions_ch = CONPLEX_ALL.out.map { id, drug, interactions -> interactions }

    NETWORK_ENRICHMENT(conplex_interactions_ch) // TODO: Localize this (use human only)
    //TISSUE DISTRIBUTION
    TISSUE_DISTRIBUTION(conplex_interactions_ch)

    biometric_ch = matched_ch.join(CONPLEX_ALL.out)

    // BIO_METRICS(GET_FINAL_METABOLITES.out,conplex_interactions_ch,conplex_drug_ch,lig_ch)
    BIO_METRICS(biometric_ch)
}