/*

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    IMPORT MODULES / SUBWORKFLOWS / FUNCTIONS

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

*/

//

// SUBWORKFLOW: Consisting of a mix of local and nf-core/modules

//

include { softwareVersionsToYAML              } from '../subworkflows/nf-core/utils_nfcore_pipeline'

include { methodsDescriptionText              } from '../subworkflows/local/utils_nfcore_deepmodeloptim_pipeline'

include { CHECK_MODEL_WF                      } from '../subworkflows/local/check_model'

include { PREPROCESS_IBIS_BEDFILE_TO_STIMULUS } from '../subworkflows/local/preprocess_ibis_bedfile_to_stimulus'

include { SPLIT_DATA_CONFIG_SPLIT_WF          } from '../subworkflows/local/split_data_config_split'

include { SPLIT_DATA_CONFIG_TRANSFORM_WF      } from '../subworkflows/local/split_data_config_transform'

include { SPLIT_CSV_WF                        } from '../subworkflows/local/split_csv'

include { TRANSFORM_CSV_WF                    } from '../subworkflows/local/transform_csv'

include { TUNE_WF                             } from '../subworkflows/local/tune'

include { EVALUATION_WF                       } from '../subworkflows/local/evaluation'

//

// MODULES: Consisting of nf-core/modules

//

include { CUSTOM_GETCHROMSIZES                } from '../modules/nf-core/custom/getchromsizes'

/*

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    RUN MAIN WORKFLOW

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

*/

workflow DEEPMODELOPTIM {

    take:

    ch_data

    ch_data_config

    ch_model

    ch_model_config

    ch_initial_weights

    ch_preprocessing_config

    ch_genome

    tune_trials_range

    tune_replicates

    prediction_data

    main:

    // TODO collect all the versions files from the different processes

    ch_versions = Channel.empty()

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

    // preprocess data

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

    if (params.preprocessing_config) {

        // create genome index

        CUSTOM_GETCHROMSIZES(ch_genome)

        ch_genome_sizes = CUSTOM_GETCHROMSIZES.out.sizes

        // preprocess bedfile into stimulus format

        PREPROCESS_IBIS_BEDFILE_TO_STIMULUS(

            ch_data,

            ch_preprocessing_config.filter{it.protocol == 'ibis'},

            ch_genome,

            ch_genome_sizes

        )

        ch_data = PREPROCESS_IBIS_BEDFILE_TO_STIMULUS.out.data

    }

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

    // split meta yaml split config file into individual yaml files

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

    SPLIT_DATA_CONFIG_SPLIT_WF( ch_data_config )

    ch_yaml_sub_config_split = SPLIT_DATA_CONFIG_SPLIT_WF.out.sub_config

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

    // split csv data file

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

    SPLIT_CSV_WF(

        ch_data,

        ch_yaml_sub_config_split

    )

    ch_split_data = SPLIT_CSV_WF.out.split_data

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

    // split meta yaml transform config file into individual yaml files

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

    SPLIT_DATA_CONFIG_TRANSFORM_WF( ch_yaml_sub_config_split )

    ch_yaml_sub_config = SPLIT_DATA_CONFIG_TRANSFORM_WF.out.sub_config

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

    // transform csv file

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

    TRANSFORM_CSV_WF(

        ch_split_data,

        ch_yaml_sub_config

    )

    ch_transformed_data = TRANSFORM_CSV_WF.out.transformed_data

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

    // check model

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

    // pre-step to check everything is fine

    // to do so we only run the first element of the sorted channel, as we don't need

    // to check on each transformed data

    // we sort the channel so that we always get the same input, as the default order

    // of the channel depends on which process finishes first (run in parallel)

    ch_check_input_data = ch_transformed_data.toSortedList().flatten().buffer(size:2).first()

    ch_check_input_config = ch_yaml_sub_config.toSortedList().flatten().buffer(size:2).first()

    CHECK_MODEL_WF (

        ch_check_input_data,

        ch_check_input_config,

        ch_model,

        ch_model_config,

        ch_initial_weights

    )

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

    // tune model

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

    // Create dependancy WF dependency to ensure TUNE_WF runs after CHECK_MODEL_WF finished

    ch_transformed_data = CHECK_MODEL_WF.out.concat(ch_transformed_data)

        .filter{it}   // remove the empty element from the check model

    TUNE_WF(

        ch_transformed_data,

        ch_yaml_sub_config,

        ch_model,

        ch_model_config,

        ch_initial_weights,

        tune_trials_range,

        tune_replicates

    )

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

    // Evaluation

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

    // Now the data config will not work if passed in full

    // We need to pass in the split data config, any of them, for the predict modules

    // This will be changed in the future

    prediction_data = prediction_data.combine(TUNE_WF.out.data_config_tmp.first().map{meta,file -> file})

    EVALUATION_WF(

        TUNE_WF.out.model_tmp,

        prediction_data

    )

    // Software versions collation remains as comments

    softwareVersionsToYAML(ch_versions)

        .collectFile(

            storeDir: "${params.outdir}/pipeline_info",

            name: 'nf_core_'  +  'deepmodeloptim_software_'  + 'versions.yml',

            sort: true,

            newLine: true

        ).set { ch_collated_versions }

    emit:

    versions = ch_versions  // channel: [ path(versions.yml) ]

}

/*

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    THE END

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

*/