comparison galaxy/tools/LC-MSMS/XSeekerPreparator.R @ 19:2937e72e5891 draft

" master branch Updating"

18:2c7e7fd1f740

TOOL_NAME <- "XSeekerPreparator"
VERSION <- "1.2.4"

DEBUG_FAST <- FALSE
DEBUG_FAST_IGNORE_SLOW_OP <- DEBUG_FAST
PROCESS_SMOL_BATCH <- DEBUG_FAST
FAST_FEATURE_RATIO <- 10

OUTPUT_SPECIFIC_TOOL <- "XSeeker_Galaxy"

ENRICHED_RDATA_VERSION <- paste("1.2.4", OUTPUT_SPECIFIC_TOOL, sep="-")
ENRICHED_RDATA_DOC <- sprintf("
Welcome to the enriched <Version %s> of the output of CAMERA/xcms.
This doc was generated by the tool: %s - Version %s
To show the different variables contained in this rdata, type:
 - `load('this_rdata.rdata', rdata_env <- new.env())`
 - `names(rdata_env)`

    - Retrieval method: enriched_rdata <- TRUE

  - enriched_rdata_version:
    - Description: A flag created by that tool to tell which version of
        this tool has enriched the rdata.
    - Retrieval method: enriched_rdata_version <- sprintf(\"%s\", ENRICHED_RDATA_VERSION)

  - enriched_rdata_doc:
    - Description: Contains the documentation string.

Data from original mzxml file

    - xcms version: 2.0

  - polarity:
    - Description: Those are the polarity values from the original mzxml
        file, extracted using xcms 2.
    - Retrieval method: as.character(xcms::xcmsRaw('original_file.mzxml')@polarity[[1]])
    - xcms version: 2.0

Data taken from incoming rdata
------
  - variableMetadata:

    - Retrieval method:
        ## just he same list, but simplified
        process_params <- list()
        for (list_name in names(rdata_file$listOFlistArguments)) {
            param_list <- list()
            for (param_name in names(rdata_file$listOFlistArguments[[list_name]])) {
                param_list[[param_name]] <- rdata_file$listOFlistArguments[[list_name]][[param_name]]
            }
            process_params[[length(process_params)+1]] <- param_list
        }
", ENRICHED_RDATA_VERSION, TOOL_NAME, VERSION, ENRICHED_RDATA_VERSION)



get_models <- function(path) {
    if (is.null(path)) {

    } else {
        message(sprintf("Loading models from %s", path))
    }
    ## galaxy mangles the "@" to a "__at__"
    if (substr(path, 1, 9) == "git__at__") {
        path <- sub("^git__at__", "git@", path, perl=TRUE)
    }
    if (
        substr(path, 1, 4) == "git@"
        || substr(path, length(path)-4, 4) == ".git"
    ) {
        return (get_models_from_git(path))
    }
    if (substr(path, 1, 4) == "http") {
        return (get_models_from_url(path))
    }
    return (source(path)$value)
}

get_models_from_git <- function (url, target_file="models.R", rm=TRUE) {
    tmp <- tempdir()
    message(sprintf("Cloning %s", url))
    system2("git", c("clone", url, tmp))
    result <- search_tree(file.path(tmp, dir), target_file)
    if (!is.null(result)) {
        models <- source(result)$value
        if (rm) {
            unlink(tmp, recursive=TRUE)
        }
        return (models)
    }
    if (rm) {
        unlink(tmp, recursive=TRUE)
    }
    stop(sprintf(
        "Could not find any file named \"%s\" in this repo",
        target_file
    ))
}

get_models_from_url <- function (url, target_file="models.R", rm=TRUE) {
    tmp <- tempdir()
    message(sprintf("Downloading %s", url))
    result <- file.path(tmp, target_file)
    if (download.file(url, destfile=result) == 0) {
        models <- source(result)$value
        if (rm) {
            unlink(tmp, recursive=TRUE)
        }
        return (models)
    }
    if (rm) {
        unlink(tmp, recursive=TRUE)
    }
    stop("Could not download any file at this adress.")
}

search_tree <- function(path, target) {
    target <- tolower(target)
    for (file in list.files(path)) {
        if (is.dir(file)) {
            result <- search_tree(file.path(path, file), target)
            if (!is.null(result)) {
                return (result)
            }
        } else if (tolower(file) == target) {
            return (file.path(path, file))
        }
    }
    return (NULL)
}

create_database <- function(orm) {
    orm$recreate_database(no_exists=FALSE)
    set_database_version(orm, "created")
}

insert_adducts <- function(orm) {
    message("Creating adducts...")
    adducts <- list(
        list("[M-H2O-H]-",1,-1,-48.992020312000001069,1,0,0.5,"H0","H1O3"),
        list("[M-H-Cl+O]-",1,-1,-19.981214542000000022,2,0,0.5,"O1","H1Cl1"),
        list("[M-Cl+O]-",1,-1,-18.973389510000000512,3,0,0.5,"O1","Cl1"),
        list("[M-3H]3-",1,-3,-3.0218293560000000219,4,0,1.0,"H0","H3"),
        list("[2M-3H]3-",2,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"),
        list("[3M-3H]3-",3,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"),
        list("[M-2H]2-",1,-2,-2.0145529039999998666,5,0,1.0,"H0","H2"),
        list("[2M-2H]2-",2,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"),
        list("[3M-2H]2-",3,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"),
        list("[M-H]-",1,-1,-1.0072764519999999333,6,1,1.0,"H0","H1"),
        list("[2M-H]-",2,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"),
        list("[3M-H]-",3,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"),
        list("[M]+",1,1,-0.00054858000000000000945,7,1,1.0,"H0","H0"),
        list("[M]-",1,-1,0.00054858000000000000945,8,1,1.0,"H0","H0"),
        list("[M+H]+",1,1,1.0072764519999999333,9,1,1.0,"H1","H0"),
        list("[2M+H]+",2,1,1.0072764519999999333,9,0,0.5,"H1","H0"),
        list("[3M+H]+",3,1,1.0072764519999999333,9,0,0.25,"H1","H0"),
        list("[M+2H]2+",1,2,2.0145529039999998666,10,0,0.75,"H2","H0"),
        list("[2M+2H]2+",2,2,2.0145529039999998666,10,0,0.5,"H2","H0"),
        list("[3M+2H]2+",3,2,2.0145529039999998666,10,0,0.25,"H2","H0"),
        list("[M+3H]3+",1,3,3.0218293560000000219,11,0,0.75,"H3","H0"),
        list("[2M+3H]3+",2,3,3.0218293560000000219,11,0,0.5,"H3","H0"),
        list("[3M+3H]3+",3,3,3.0218293560000000219,11,0,0.25,"H3","H0"),
        list("[M-2H+NH4]-",1,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"),
        list("[2M-2H+NH4]-",2,-1,16.019272654000001665,12,0,0.0,"N1H4","H2"),
        list("[3M-2H+NH4]-",3,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"),
        list("[M+NH4]+",1,1,18.033825558000000199,13,1,1.0,"N1H4","H0"),
        list("[2M+NH4]+",2,1,18.033825558000000199,13,0,0.5,"N1H4","H0"),
        list("[3M+NH4]+",3,1,18.033825558000000199,13,0,0.25,"N1H4","H0"),
        list("[M+H+NH4]2+",1,2,19.041102009999999467,14,0,0.5,"N1H5","H0"),
        list("[2M+H+NH4]2+",2,2,19.041102009999999467,14,0,0.5,"N1H5","H0"),
        list("[3M+H+NH4]2+",3,2,19.041102009999999467,14,0,0.25,"N1H5","H0"),
        list("[M+Na-2H]-",1,-1,20.974668176000001551,15,0,0.75,"Na1","H2"),
        list("[2M-2H+Na]-",2,-1,20.974668176000001551,15,0,0.25,"Na1","H2"),
        list("[3M-2H+Na]-",3,-1,20.974668176000001551,15,0,0.25,"Na1","H2"),
        list("[M+Na]+",1,1,22.989221080000000086,16,1,1.0,"Na1","H0"),
        list("[2M+Na]+",2,1,22.989221080000000086,16,0,0.5,"Na1","H0"),
        list("[3M+Na]+",3,1,22.989221080000000086,16,0,0.25,"Na1","H0"),
        list("[M+H+Na]2+",1,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"),
        list("[2M+H+Na]2+",2,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"),
        list("[3M+H+Na]2+",3,2,23.996497531999999353,17,0,0.25,"Na1H1","H0"),
        list("[M+2H+Na]3+",1,3,25.003773983999998619,18,0,0.25,"H2Na1","H0"),
        list("[M+CH3OH+H]+",1,1,33.033491200000000276,19,0,0.25,"C1O1H5","H0"),
        list("[M-H+Cl]2-",1,-2,33.962124838000001148,20,0,1.0,"Cl1","H1"),
        list("[2M-H+Cl]2-",2,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"),
        list("[3M-H+Cl]2-",3,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"),
        list("[M+Cl]-",1,-1,34.969401290000000416,21,1,1.0,"Cl1","H0"),
        list("[2M+Cl]-",2,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"),
        list("[3M+Cl]-",3,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"),
        list("[M+K-2H]-",1,-1,36.948605415999999479,22,0,0.5,"K1","H2"),
        list("[2M-2H+K]-",2,-1,36.948605415999999479,22,0,0.0,"K1","H2"),
        list("[3M-2H+K]-",3,-1,36.948605415999999479,22,0,0.0,"K1","H2"),
        list("[M+K]+",1,1,38.963158319999998013,23,1,1.0,"K1","H0"),
        list("[2M+K]+",2,1,38.963158319999998013,23,0,0.5,"K1","H0"),
        list("[3M+K]+",3,1,38.963158319999998013,23,0,0.25,"K1","H0"),
        list("[M+H+K]2+",1,2,39.970434771999997281,24,0,0.5,"K1H1","H0"),
        list("[2M+H+K]2+",2,2,39.970434771999997281,24,0,0.5,"K1H1","H0"),
        list("[3M+H+K]2+",3,2,39.970434771999997281,24,0,0.25,"K1H1","H0"),
        list("[M+ACN+H]+",1,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"),
        list("[2M+ACN+H]+",2,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"),
        list("[M+2Na-H]+",1,1,44.971165708000000902,26,0,0.5,"Na2","H1"),
        list("[2M+2Na-H]+",2,1,44.971165708000000902,26,0,0.25,"Na2","H1"),
        list("[3M+2Na-H]+",3,1,44.971165708000000902,26,0,0.25,"Na2","H1"),
        list("[2M+FA-H]-",2,-1,44.998202851999998586,27,0,0.25,"C1O2H2","H1"),
        list("[M+FA-H]-",1,-1,44.998202851999998586,27,0,0.5,"C1O2H2","H1"),
        list("[M+2Na]2+",1,2,45.978442160000000172,28,0,0.5,"Na2","H0"),
        list("[2M+2Na]2+",2,2,45.978442160000000172,28,0,0.5,"Na2","H0"),
        list("[3M+2Na]2+",3,2,45.978442160000000172,28,0,0.25,"Na2","H0"),
        list("[M+H+2Na]3+",1,3,46.985718611999999438,29,0,0.25,"H1Na2","H0"),
        list("[M+H+FA]+",1,1,47.012755755999997122,30,0,0.25,"C1O2H3","H0"),
        list("[M+Hac-H]-",1,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"),
        list("[2M+Hac-H]-",2,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"),
        list("[M+IsoProp+H]+",1,1,61.064791327999998317,32,0,0.25,"C3H9O1","H0"),
        list("[M+Na+K]2+",1,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"),
        list("[2M+Na+K]2+",2,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"),
        list("[3M+Na+K]2+",3,2,61.9523793999999981,33,0,0.25,"Na1K1","H0"),
        list("[M+NO3]-",1,-1,61.988366450000000895,34,0,0.5,"N1O3","H0"),
        list("[M+ACN+Na]+",1,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"),
        list("[2M+ACN+Na]+",2,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"),
        list("[M+NH4+FA]+",1,1,64.039304861999994502,36,0,0.25,"N1C1O2H6","H0"),
        list("[M-2H+Na+FA]-",1,-1,66.980147479999999405,37,0,0.5,"NaC1O2H2","H2"),
        list("[M+3Na]3+",1,3,68.967663239999993153,38,0,0.25,"Na3","H0"),
        list("[M+Na+FA]+",1,1,68.99470038399999794,39,0,0.25,"Na1C1O2H2","H0"),
        list("[M+2Cl]2-",1,-2,69.938802580000000832,40,0,1.0,"Cl2","H0"),
        list("[2M+2Cl]2-",2,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"),
        list("[3M+2Cl]2-",3,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"),
        list("[M+2K-H]+",1,1,76.919040187999996758,41,0,0.5,"K2","H1"),
        list("[2M+2K-H]+",2,1,76.919040187999996758,41,0,0.25,"K2","H1"),
        list("[3M+2K-H]+",3,1,76.919040187999996758,41,0,0.25,"K2","H1"),
        list("[M+2K]2+",1,2,77.926316639999996028,42,0,0.5,"K2","H0"),
        list("[2M+2K]2+",2,2,77.926316639999996028,42,0,0.5,"K2","H0"),
        list("[3M+2K]2+",3,2,77.926316639999996028,42,0,0.25,"K2","H0"),
        list("[M+Br]-",1,-1,78.918886479999997619,43,1,1.0,"Br1","H0"),
        list("[M+Cl+FA]-",1,-1,80.974880593999998268,44,0,0.5,"Cl1C1O2H2","H0"),
        list("[M+AcNa-H]-",1,-1,80.995797543999998426,45,0,0.25,"C2H3Na1O2","H1"),
        list("[M+2ACN+2H]2+",1,2,84.067651115999993292,46,0,0.25,"C4H8N2","H0"),
        list("[M+K+FA]+",1,1,84.968637623999995868,47,0,0.25,"K1C1O2H2","H0"),
        list("[M+Cl+Na+FA-H]-",1,-1,102.95682522200000619,48,0,0.5,"Cl1Na1C1O2H2","H1"),
        list("[2M+3H2O+2H]+",2,1,104.03153939599999944,49,0,0.25,"H8O6","H0"),
        list("[M+TFA-H]-",1,-1,112.98558742000000165,50,0,0.5,"C2F3O2H1","H1"),
        list("[M+H+TFA]+",1,1,115.00014032400000019,51,0,0.25,"C2F3O2H2","H0"),
        list("[M+3ACN+2H]2+",1,2,125.09420022199999778,52,0,0.25,"C6H11N3","H0"),
        list("[M+NH4+TFA]+",1,1,132.02668943000000468,53,0,0.25,"N1C2F3O2H5","H0"),
        list("[M+Na+TFA]+",1,1,136.98208495200000811,54,0,0.25,"Na1C2F3O2H1","H0"),
        list("[M+Cl+TFA]-",1,-1,148.96226516199999423,55,0,0.5,"Cl1C2F3O2H1","H0"),
        list("[M+K+TFA]+",1,1,152.95602219200000604,56,0,0.25,"K1C2F3O2H1","H0")
    )
    dummy_adduct <- orm$adduct()
    for (adduct in adducts) {
        i <- 0
        dummy_adduct$set_name(adduct[[i <- i+1]])
        dummy_adduct$set_multi(adduct[[i <- i+1]])
        dummy_adduct$set_charge(adduct[[i <- i+1]])
        dummy_adduct$set_mass(adduct[[i <- i+1]])
        dummy_adduct$set_oidscore(adduct[[i <- i+1]])
        dummy_adduct$set_quasi(adduct[[i <- i+1]])
        dummy_adduct$set_ips(adduct[[i <- i+1]])
        dummy_adduct$set_formula_add(adduct[[i <- i+1]])
        dummy_adduct$set_formula_ded(adduct[[i <- i+1]])
        invisible(dummy_adduct$save())
        dummy_adduct$clear(unset_id=TRUE)
    }
    message("Adducts created")
}

insert_base_data <- function(orm, path, archetype=FALSE) {
    if (archetype) {
        ## not implemented yet
        return ()
    }
    base_data <- readLines(path)
    for (sql in strsplit(paste(base_data, collapse=" "), ";")[[1]]) {
        orm$execute(sql)
    }
    set_database_version(orm, "enriched")
}

insert_compounds <- function(orm, compounds_path) {
    compounds <- read.csv(file=compounds_path, sep="\t")
    if (is.null(compounds <- translate_compounds(compounds))) {
        stop("Could not find asked compound's attributes in csv file.")
    }
    dummy_compound <- orm$compound()
    compound_list <- list()
    for (i in seq_len(nrow(compounds))) {
        dummy_compound$set_mz(compounds[i, "mz"])
        dummy_compound$set_name(compounds[i, "name"])
        dummy_compound$set_common_name(compounds[i, "common_name"])
        dummy_compound$set_formula(compounds[i, "formula"])
        compound_list[[length(compound_list)+1]] <- as.list(
            dummy_compound,
            c("mz", "name", "common_name", "formula")
        )
        dummy_compound$clear(unset_id=TRUE)
    }
    invisible(dummy_compound$save(bulk=compound_list))
}

translate_compounds <- function(compounds) {
    recognized_headers <- list(
        c("HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1", "MetName", "ChemFormula", "INChIkey")
    )
    header_translators <- list(
        hmdb_header_translator
    )
    for (index in seq_along(recognized_headers)) {
        headers <- recognized_headers[[index]]
        if (identical(colnames(compounds), headers)) {
            return (header_translators[[index]](compounds))
        }
    }
    if (is.null(translator <- guess_translator(colnames(compounds)))) {
        return (NULL)
    }
    return (csv_header_translator(translator, compounds))
}

guess_translator <- function(header) {
    result <- list(
        # HMDB_ID=NULL,
        mz=NULL,
        name=NULL,
        common_name=NULL,
        formula=NULL,
        # inchi_key=NULL
    )
    asked_cols <- names(result)
    for (asked_col in asked_cols) {
        for (col in header) {
            if ((twisted <- tolower(col)) == asked_col

                next
            }
        }
    }
    if (any(mapply(is.null, result))) {
        return (NULL)
    }
    return (result)
}

hmdb_header_translator <- function(compounds) {
    return (csv_header_translator(
        list(
            HMDB_ID="HMDB_ID",
            mz="MzBank",
            name="MetName",
            common_name="MetName",
            formula="ChemFormula",
            inchi_key="INChIkey"
        ), compounds
    ))
}

csv_header_translator <- function(translation_table, csv) {
    header_names <- names(translation_table)
    result <- data.frame(1:nrow(csv))
    for (i in seq_along(header_names)) {
        result[, header_names[[i]]] <- csv[, translation_table[[i]]]
    }
    result[, "mz"] <- as.numeric(result[, "mz"])
    return (result)
}

set_database_version <- function(orm, version) {
    orm$set_tag(
        version,
        tag_name="database_version",
        tag_table_name="XSeeker_tagging_table"
    )
}

process_rdata <- function(orm, rdata, options) {
    mzml_tmp_dir <- gather_mzml_files(rdata)

        || options$`not-show-percent` == FALSE
    )
    error <- tryCatch({
        process_sample_list(
            orm, rdata, samples,
            show_percent=show_percent
        )
        NULL
    }, error=function(e) {
        message(e)
        e
    })
    if (!is.null(mzml_tmp_dir)) {
        unlink(mzml_tmp_dir, recursive=TRUE)
    }
    if (!is.null(error)) {
        stop(error)
    }
}

gather_mzml_files <- function(rdata) {
    if (is.null(rdata$singlefile)) {
        message("Extracting mxml files")
        tmp <- tempdir()
        rdata$singlefile <- utils::unzip(rdata$zipfile, exdir=tmp)
        names(rdata$singlefile) <- tools::file_path_sans_ext(basename(rdata$singlefile))
        message("Extracted")
        return (tmp)
    } else {
        message(sprintf("Not a zip file, loading files directly from path: %s", paste(rdata$singlefile, collapse=" ; ")))
    }
    return (NULL)
}

process_sample_list <- function(orm, radta, sample_names, show_percent) {
    file_grouping_var <- find_grouping_var(rdata$variableMetadata)
    message("Processing samples.")
    message(sprintf("File grouping variable: %s", file_grouping_var))
    if(is.null(file_grouping_var)) {
        stop("Malformed variableMetada.")
    }

    context <- new.env()
    context$samples <- list()
    context$peaks <- rdata$xa@xcmsSet@peaks
    context$groupidx <- rdata$xa@xcmsSet@groupidx

    process_arg_list <- rdata$listOFlistArguments
    var_meta <- rdata$variableMetadata

    process_params <- list()
    if (is.null(process_arg_list)) {
        histories <- list()
        for (history in xcms_set@.processHistory) {
            if (
                class(history@param) == "CentWaveParam"
                && history@type == "Peak detection"
            ) {
                params <- history@param
                process_params <- list(list(
                    xfunction="annotatediff",
                    ppm=params@ppm,
                    peakwidth=sprintf("%s - %s", params@peakwidth[[1]], params@peakwidth[[2]]),
                    snthresh=params@snthresh,
                    prefilterStep=params@prefilter[[1]],
                    prefilterLevel=params@prefilter[[2]],
                    mzdiff=params@mzdiff,
                    fitgauss=params@fitgauss,
                    noise=params@noise,
                    mzCenterFun=params@mzCenterFun,
                    integrate=params@integrate,
                    firstBaselineCheck=params@firstBaselineCheck,
                    snthreshIsoROIs=!identical(params@roiScales, numeric(0))
                ))
                break
            }
        }
    } else {
        for (list_name in names(process_arg_list)) {
            param_list <- list()
            for (param_name in names(process_arg_list[[list_name]])) {
                param_list[[param_name]] <- process_arg_list[[list_name]][[param_name]]
            }
            process_params[[length(process_params)+1]] <- param_list
        }
    }

    message("Parameters from previous processes extracted.")


    indices <- as.numeric(unique(var_meta[, file_grouping_var]))
    smol_xcms_set <- orm$smol_xcms_set()
    mz_tab_info <- new.env()
    g <- xcms::groups(xcms_set)
    mz_tab_info$group_length <- nrow(g)
    mz_tab_info$dataset_path <- xcms::filepaths(xcms_set)
    mz_tab_info$sampnames <- xcms::sampnames(xcms_set)
    mz_tab_info$sampclass <- xcms::sampclass(xcms_set)
    mz_tab_info$rtmed <- g[,"rtmed"]
    mz_tab_info$mzmed <- g[,"mzmed"]
    mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(xcms_set, value="into")
    blogified <- blob::blob(fst::compress_fst(serialize(mz_tab_info, NULL), compression=100))
    rm(mz_tab_info)

    invisible(smol_xcms_set$set_raw(blogified)$save())
    smol_xcms_set_id <- smol_xcms_set$get_id()
    rm(smol_xcms_set)

        env$enriched_rdata_version <- ENRICHED_RDATA_VERSION
        env$tool_name <- TOOL_NAME
        env$enriched_rdata_doc <- ENRICHED_RDATA_DOC

        sample <- add_sample_to_database(orm, env, context, smol_xcms_set_id)
        rm (env)
        context$samples[no] <- sample$get_id()
        rm (sample)
    }
    context$clusters <- list()
    context$show_percent <- show_percent
    context$cluster_mean_rt_abundance <- list()
    context$central_feature <- list()

    clusters <- context$clusters
    rm(context)
    message("Features enrichment")
    complete_features(orm, clusters, show_percent)
    message("Features enrichment done.")
    return (NULL)
}

find_grouping_var <- function(var_meta) {
    known_colnames = c(
        "name", "namecustom", "mz", "mzmin", "mzmax",
        "rt", "rtmin", "rtmax", "npeaks", "isotopes", "adduct", "pcgroup"
    )
    col_names <- colnames(var_meta)
    classes = list()
    for (name in col_names) {
        if (!(name %in% known_colnames)) {
            classes[[length(classes)+1]] = name
        }
    }
    if (length(classes) > 1) {
        stop(sprintf("Only one class expected in the variable metadata. Found %d .", length(classes)))
    }
    if (length(classes) == 0) {
        stop("Could not find any class column in your variableMetadata.")
    }
    return (classes[[1]])
}

add_sample_to_database <- function(orm, env, context, smol_xcms_set_id) {
    message(sprintf("Processing sample %s", env$sample_name))
    sample <- (
        orm$sample()
        $set_name(env$sample_name)
        $set_path(env$dataset_path)
        $set_kind("enriched_rdata")
        $set_polarity(
            if (is.null(env$polarity) || identical(env$polarity, character(0))) ""
            else env$polarity
        )
        $set_raw(blob::blob(fst::compress_fst(
            serialize(env, NULL),
            compression=100
        )))
    )
    sample[["smol_xcms_set_id"]] <- smol_xcms_set_id
    sample$modified__[["smol_xcms_set_id"]] <- smol_xcms_set_id
    sample <- sample$save()
    load_process_params(orm, sample, env$process_params)
    message(sprintf("Sample %s inserted.", env$sample_name))
    return (sample)
}


load_variable_metadata <- function(orm, var_meta, context) {
    all_clusters <- orm$cluster()$all()

        orm, var_meta, context,
        next_feature_id, next_cluster_id,
        next_pc_group, next_align_group
    ))
    message("Extracting features done.")
    return (NULL)
}

get_next_id <- function(models, attribute) {
    if ((id <- models$max(attribute)) == Inf || id == -Inf) {
        return (0)
    }
    return (id)
}

create_features <- function(
    orm, var_meta, context,
    next_feature_id, next_cluster_id,
    next_pc_group, next_align_group
) {
    field_names <- as.list(names(orm$feature()$fields__))
    field_names[field_names=="id"] <- NULL

    features <- list()
    dummy_feature <- orm$feature()

    if (show_percent <- context$show_percent) {

        total <- nrow(var_meta)
    }
    rows <- seq_len(nrow(var_meta))
    if (PROCESS_SMOL_BATCH) {

        rows <- rows[1:as.integer(FAST_FEATURE_RATIO/100.0 * length(rows))]
    }
    cluster_row <- list()
    for (row in rows) {
        if (show_percent && (row / total) * 100 > percent) {
            percent <- percent + 1
            message("\r", sprintf("\r%d %%", percent), appendLF=FALSE)
        }

        dummy_feature$set_featureID(next_feature_id)
        next_feature_id <- next_feature_id + 1

        context$clusterID <- clusterID
        dummy_feature$set_iso(iso)

        peak_list <- context$peaks[context$groupidx[[row]], ]
        if (! ("matrix" %in% class(peak_list))) {
            peak_list <- matrix(peak_list, nrow=1, ncol=length(peak_list), dimnames=list(c(), names(peak_list)))
        }

        clusterID <- as.character(clusterID)
        if (is.null(context$central_feature[[clusterID]])) {
            int_o <- extract_peak_var(peak_list, "into")
            context$central_feature[[clusterID]] <- (
                peak_list[peak_list[, "into"] == int_o,]["sample"]
            )
        }

        if (!DEBUG_FAST_IGNORE_SLOW_OP) {
            sample_peak_list <- peak_list[as.integer(peak_list[, "sample"]) == context$central_feature[[clusterID]], , drop=FALSE]
            if (!identical(sample_peak_list, numeric(0)) && !is.null(nrow(sample_peak_list)) && nrow(sample_peak_list) != 0) {
                if (!is.na(int_o <- extract_peak_var(sample_peak_list, "into"))) {
                    dummy_feature$set_int_o(int_o)
                }
                if (!is.na(int_b <- extract_peak_var(sample_peak_list, "intb"))) {
                    dummy_feature$set_int_b(int_b)
                }
                if (!is.na(max_o <- extract_peak_var(sample_peak_list, "maxo"))) {
                    dummy_feature$set_max_o(max_o)
                }
            }
        }

            dummy_feature, clusterID,
            context, curent_var_meta, next_pc_group,
            next_align_group
        )
        next_align_group <- next_align_group + 1
        features[[length(features)+1]] <- as.list(dummy_feature, field_names)
        dummy_feature$clear()
    }
    rm(var_meta)
    message("")
    message("Saving features")
    invisible(dummy_feature$save(bulk=features))

    ## We link manually clusters to the sample they're in.
    link_cache <- list()
    for (row in rows) {
        sample_nos <- unique(context$peaks[context$groupidx[[row]], "sample"])
        for (sample_id in context$samples[sample_nos]) {
            cluster_id <- cluster_row[[row]]$get_id()
            if (is.null(link_cache[[id <- paste(sample_id, cluster_id, sep=";")]])) {
                link_cache[[id]] <- 1
                orm$cluster_sample(
                    sample_id=sample_id,
                    cluster_id=cluster_id
                )$save()
            }
        }
    }

    message("Saved.")
    return (context$clusters)
}

extract_peak_var <- function(peak_list, var_name, selector=max) {
    value <- peak_list[, var_name]
    names(value) <- NULL
    return (selector(value))
}

set_feature_fields_from_var_meta <- function(feature, var_meta) {
    if (!is.null(mz <- var_meta[["mz"]]) && !is.na(mz)) {
        feature$set_mz(mz)

        feature$set_rt_max(rtmax)
    }
    if (!is.null(isotopes <- var_meta[["isotopes"]]) && !is.na(isotopes)) {
        feature$set_iso(isotopes)
    }
    return (feature)
}

extract_iso  <- function(weird_data) {
    if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
        return (sub("^\\[\\d+\\]", "", weird_data, perl=TRUE))
    }
    return (weird_data)
}

extract_clusterID <- function(weird_data, next_cluster_id){
    if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
        clusterID <- stringr::str_extract(weird_data, "^\\[\\d+\\]")
        clusterID <- as.numeric(stringr::str_extract(clusterID, "\\d+"))
    } else {
        clusterID <- 0
    }
    return (clusterID + next_cluster_id)
}

create_associated_cluster <- function(
    orm,
    main_sample_id, feature, clusterID,

    if (is.null(cluster <- context$clusters[[clusterID]])) {
        pcgroup <- as.numeric(curent_var_meta[["pcgroup"]])
        adduct_name <- as.character(curent_var_meta[["adduct"]])
        annotation <- curent_var_meta[["isotopes"]]
        cluster <- context$clusters[[clusterID]] <- orm$cluster(
            pc_group=pcgroup + next_pc_group,
            # adduct=adduct,
            align_group=next_align_group,
            # curent_group=curent_group,
            clusterID=context$clusterID,
            annotation=annotation
        )
        if (is.null(adduct <- context$adducts[[adduct_name]])) {
            context$adducts[[adduct_name]] <- orm$adduct()$load_by(name=adduct_name)$first()
            if (is.null(adduct <- context$adducts[[adduct_name]])) {
                adduct <- context$adducts[[adduct_name]] <- orm$adduct(name=adduct_name, charge=0)
                adduct$save()
            }
        }
        cluster$set_adduct(adduct)
        ## Crappy hack to assign sample id to cluster without loading the sample.
        ## Samples are too big (their sample$env) and slows the process, and eat all the menory
        ## so we dont't want to load them.
        cluster[["sample_id"]] <- main_sample_id
        cluster$modified__[["sample_id"]] <- main_sample_id
    } else {
        if (context$clusterID != 0 && cluster$get_clusterID() == 0) {
            cluster$set_clusterID(context$clusterID)
        }
    }
    cluster$save()
    feature$set_cluster(cluster)
    return (cluster)
}

complete_features <- function(orm, clusters, show_percent) {
    total <- length(clusters)
    percent <- -1
    i <- 0
    for (cluster in clusters) {
        i <- i+1
        if (show_percent && (i / total) * 100 > percent) {
            percent <- percent + 1
            message("\r", sprintf("\r%d %%", percent), appendLF=FALSE)
        }
        features <- orm$feature()$load_by(cluster_id=cluster$get_id())
        if (features$any()) {
            if (!is.null(rt <- features$mean("rt"))) {
                cluster$set_mean_rt(rt)$save()
            }
            features_df <- as.data.frame(features)
            central_feature <- features_df[grepl("^\\[M\\]", features_df[, "iso"]), ]
            central_feature_into <- central_feature[["int_o"]]
            if (!identical(central_feature_into, numeric(0)) && central_feature_into != 0) {
                for (feature in as.vector(features)) {
                    feature$set_abundance(
                        feature$get_int_o() / central_feature_into * 100
                    )$save()
                }
            }
        }
    }
    return (NULL)
}

load_process_params <- function(orm, sample, params) {
    for (param_list in params) {
        if (is.null(param_list[["xfunction"]])) {

        }
        if (param_list[["xfunction"]] == "annotatediff") {
            load_process_params_peak_picking(orm, sample, param_list)
        }
    }
    return (sample)
}

load_process_params_peak_picking <- function(orm, sample, peak_picking_params) {
    return (add_sample_process_parameters(
        params=peak_picking_params,
        params_translation=list(
            ppm="ppm",
            maxcharge="maxCharge",
            maxiso="maxIso"
        ),
        param_model_generator=orm$peak_picking_parameters,
        sample_param_setter=sample$set_peak_picking_parameters
    ))
}

add_sample_process_parameters <- function(
    params,

        params_model <- params_models$first()
    } else {
        params_model <- do.call(param_model_generator, model_params)
        params_model$save()
    }
    return (sample_param_setter(params_model)$save())
}


library(optparse)

option_list <- list(
    optparse::make_option(
        c("-v", "--version"),
        action="store_true",
        help="Display this tool's version and exits"
    ),
    optparse::make_option(
        c("-i", "--input"),
        type="character",
        help="The rdata path to import in XSeeker"
    ),
    optparse::make_option(
        c("-s", "--samples"),
        type="character",
        help="Samples to visualise in XSeeker"
    ),
    optparse::make_option(
        c("-B", "--archetype"),
        type="character",
        help="The name of the base database"
    ),
    optparse::make_option(
        c("-b", "--database"),
        type="character",
        help="The base database's path"
    ),
    optparse::make_option(
        c("-c", "--compounds-csv"),
        type="character",
        help="The csv containing compounds"
    ),
    optparse::make_option(
        c("-m", "--models"),
        type="character",
        help="The path or url (must begin with http[s]:// or git@) to the database's models"
    ),
    optparse::make_option(
        c("-o", "--output"),
        type="character",
        help="The path where to output sqlite"
    ),
    optparse::make_option(
        c("-P", "--not-show-percent"),
        action="store_true",
        help="Flag not to show the percents",
        default=FALSE
    )
)

options(error=function(){traceback(3)})

parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
args <- parse_args(parser, positional_arguments=0)

err_code <- 0

if (!is.null(args$options$version)) {
    message(sprintf("%s %s", TOOL_NAME, VERSION))
    quit()
}

models <- get_models(args$options$models)
orm <- DBModelR::ORM(
    connection_params=list(dbname=args$options$output),
    dbms="SQLite"
)

invisible(orm$models(models))
invisible(create_database(orm))

    insert_base_data(orm, args$options$database)
}
message(sprintf("Base data inserted using %s.", args$options$database))

if (!is.null(args$options$archetype)) {
    insert_base_data(orm, args$options$archetype, archetype=TRUE)
}
if (!is.null(args$options$`compounds-csv`)) {
    insert_compounds(orm, args$options$`compounds-csv`)
}

load(args$options$input, rdata <- new.env())

process_rdata(orm, rdata, args$options)

quit(status=err_code)

19:2937e72e5891

assign("TOOL_NAME", "XSeekerPreparator", envir = globalenv())
lockBinding("TOOL_NAME", globalenv())
assign("VERSION", "1.3.0", envir = globalenv())
lockBinding("VERSION", globalenv())
assign("DEBUG_FAST", FALSE, envir = globalenv())
lockBinding("DEBUG_FAST", globalenv())
assign("DEBUG_FAST_IGNORE_SLOW_OP", DEBUG_FAST, envir = globalenv())
lockBinding("DEBUG_FAST_IGNORE_SLOW_OP", globalenv())
assign("PROCESS_SMOL_BATCH", DEBUG_FAST, envir = globalenv())
lockBinding("PROCESS_SMOL_BATCH", globalenv())
assign("FAST_FEATURE_RATIO", 10, envir = globalenv())
lockBinding("FAST_FEATURE_RATIO", globalenv())
assign("OUTPUT_SPECIFIC_TOOL", "XSeeker_Galaxy", envir = globalenv())
lockBinding("OUTPUT_SPECIFIC_TOOL", globalenv())

assign(
    "ENRICHED_RDATA_VERSION",
    paste(VERSION, OUTPUT_SPECIFIC_TOOL, sep = "-"),
    envir = globalenv()
)
lockBinding("ENRICHED_RDATA_VERSION", globalenv())
assign("ENRICHED_RDATA_DOC", sprintf("
Welcome to the enriched <Version %s> of the output of CAMERA/xcms.
This doc was generated by the tool: %s - Version %s
To show the different variables contained in this rdata, type:
 - `load('this_rdata.rdata', rdata_env <- new.env())`
 - `names(rdata_env)`

    - Retrieval method: enriched_rdata <- TRUE

  - enriched_rdata_version:
    - Description: A flag created by that tool to tell which version of
        this tool has enriched the rdata.
    - Retrieval method:
      enriched_rdata_version <- sprintf(
          \"%s\",
          ENRICHED_RDATA_VERSION
      )

  - enriched_rdata_doc:
    - Description: Contains the documentation string.

Data from original mzxml file

    - xcms version: 2.0

  - polarity:
    - Description: Those are the polarity values from the original mzxml
        file, extracted using xcms 2.
    - Retrieval method:
        as.character(xcms::xcmsRaw(
            'original_file.mzxml'
        )@polarity[[1]])
    - xcms version: 2.0

Data taken from incoming rdata
------
  - variableMetadata:

    - Retrieval method:
        ## just he same list, but simplified
        process_params <- list()
        for (list_name in names(rdata_file$listOFlistArguments)) {
            param_list <- list()
            for (param_name in names(
                    rdata_file$listOFlistArguments[[list_name]]
            )) {
                param_list[[param_name]] <- rdata_file$listOFlistArguments[[
                    list_name
                ]][[param_name]]
            }
            process_params[[length(process_params)+1]] <- param_list
        }
", ENRICHED_RDATA_VERSION, TOOL_NAME, VERSION, ENRICHED_RDATA_VERSION),
envir = globalenv())
lockBinding("ENRICHED_RDATA_DOC", globalenv())



get_models <- function(path) {
    if (is.null(path)) {

    } else {
        message(sprintf("Loading models from %s", path))
    }
    ## galaxy mangles the "@" to a "__at__"
    if (substr(path, 1, 9) == "git__at__") {
        path <- sub("^git__at__", "git@", path, perl = TRUE)
    }
    if (
        substr(path, 1, 4) == "git@"
        || substr(path, length(path) - 4, 4) == ".git"
    ) {
        return(get_models_from_git(path))
    }
    if (substr(path, 1, 4) == "http") {
        return(get_models_from_url(path))
    }
    return(source(path)$value)
}

get_models_from_git <- function(url, target_file = "models.R", rm = TRUE) {
    tmp <- tempdir()
    message(sprintf("Cloning %s", url))
    system2("git", c("clone", url, tmp))
    result <- search_tree(file.path(tmp, dir), target_file)
    if (!is.null(result)) {
        models <- source(result)$value
        if (rm) {
            unlink(tmp, recursive = TRUE)
        }
        return(models)
    }
    if (rm) {
        unlink(tmp, recursive = TRUE)
    }
    stop(sprintf(
        "Could not find any file named \"%s\" in this repo",
        target_file
    ))
}

get_models_from_url <- function(url, target_file = "models.R", rm = TRUE) {
    tmp <- tempdir()
    message(sprintf("Downloading %s", url))
    result <- file.path(tmp, target_file)
    if (download.file(url, destfile = result) == 0) {
        models <- source(result)$value
        if (rm) {
            unlink(tmp, recursive = TRUE)
        }
        return(models)
    }
    if (rm) {
        unlink(tmp, recursive = TRUE)
    }
    stop("Could not download any file at this adress.")
}

search_tree <- function(path, target) {
    target <- tolower(target)
    for (file in list.files(path)) {
        if (is.dir(file)) {
            result <- search_tree(file.path(path, file), target)
            if (!is.null(result)) {
                return(result)
            }
        } else if (tolower(file) == target) {
            return(file.path(path, file))
        }
    }
    return(NULL)
}

create_database <- function(orm) {
    orm$recreate_database(no_exists = FALSE)
    set_database_version(orm, "created")
}

insert_adducts <- function(orm) {
    message("Creating adducts...")
    adducts <- list(
        list("[M-H2O-H]-", 1, -1, -48.992020312000001069, 1, 0, 0.5, "H0", "H1O3"),
        list("[M-H-Cl+O]-", 1, -1, -19.981214542000000022, 2, 0, 0.5, "O1", "H1Cl1"),
        list("[M-Cl+O]-", 1, -1, -18.973389510000000512, 3, 0, 0.5, "O1", "Cl1"),
        list("[M-3H]3-", 1, -3, -3.0218293560000000219, 4, 0, 1.0, "H0", "H3"),
        list("[2M-3H]3-", 2, -3, -3.0218293560000000219, 4, 0, 0.5, "H0", "H3"),
        list("[3M-3H]3-", 3, -3, -3.0218293560000000219, 4, 0, 0.5, "H0", "H3"),
        list("[M-2H]2-", 1, -2, -2.0145529039999998666, 5, 0, 1.0, "H0", "H2"),
        list("[2M-2H]2-", 2, -2, -2.0145529039999998666, 5, 0, 0.5, "H0", "H2"),
        list("[3M-2H]2-", 3, -2, -2.0145529039999998666, 5, 0, 0.5, "H0", "H2"),
        list("[M-H]-", 1, -1, -1.0072764519999999333, 6, 1, 1.0, "H0", "H1"),
        list("[2M-H]-", 2, -1, -1.0072764519999999333, 6, 0, 0.5, "H0", "H1"),
        list("[3M-H]-", 3, -1, -1.0072764519999999333, 6, 0, 0.5, "H0", "H1"),
        list("[M]+", 1, 1, -0.00054858000000000000945, 7, 1, 1.0, "H0", "H0"),
        list("[M]-", 1, -1, 0.00054858000000000000945, 8, 1, 1.0, "H0", "H0"),
        list("[M+H]+", 1, 1, 1.0072764519999999333, 9, 1, 1.0, "H1", "H0"),
        list("[2M+H]+", 2, 1, 1.0072764519999999333, 9, 0, 0.5, "H1", "H0"),
        list("[3M+H]+", 3, 1, 1.0072764519999999333, 9, 0, 0.25, "H1", "H0"),
        list("[M+2H]2+", 1, 2, 2.0145529039999998666, 10, 0, 0.75, "H2", "H0"),
        list("[2M+2H]2+", 2, 2, 2.0145529039999998666, 10, 0, 0.5, "H2", "H0"),
        list("[3M+2H]2+", 3, 2, 2.0145529039999998666, 10, 0, 0.25, "H2", "H0"),
        list("[M+3H]3+", 1, 3, 3.0218293560000000219, 11, 0, 0.75, "H3", "H0"),
        list("[2M+3H]3+", 2, 3, 3.0218293560000000219, 11, 0, 0.5, "H3", "H0"),
        list("[3M+3H]3+", 3, 3, 3.0218293560000000219, 11, 0, 0.25, "H3", "H0"),
        list("[M-2H+NH4]-", 1, -1, 16.019272654000001665, 12, 0, 0.25, "N1H4", "H2"),
        list("[2M-2H+NH4]-", 2, -1, 16.019272654000001665, 12, 0, 0.0, "N1H4", "H2"),
        list("[3M-2H+NH4]-", 3, -1, 16.019272654000001665, 12, 0, 0.25, "N1H4", "H2"),
        list("[M+NH4]+", 1, 1, 18.033825558000000199, 13, 1, 1.0, "N1H4", "H0"),
        list("[2M+NH4]+", 2, 1, 18.033825558000000199, 13, 0, 0.5, "N1H4", "H0"),
        list("[3M+NH4]+", 3, 1, 18.033825558000000199, 13, 0, 0.25, "N1H4", "H0"),
        list("[M+H+NH4]2+", 1, 2, 19.041102009999999467, 14, 0, 0.5, "N1H5", "H0"),
        list("[2M+H+NH4]2+", 2, 2, 19.041102009999999467, 14, 0, 0.5, "N1H5", "H0"),
        list("[3M+H+NH4]2+", 3, 2, 19.041102009999999467, 14, 0, 0.25, "N1H5", "H0"),
        list("[M+Na-2H]-", 1, -1, 20.974668176000001551, 15, 0, 0.75, "Na1", "H2"),
        list("[2M-2H+Na]-", 2, -1, 20.974668176000001551, 15, 0, 0.25, "Na1", "H2"),
        list("[3M-2H+Na]-", 3, -1, 20.974668176000001551, 15, 0, 0.25, "Na1", "H2"),
        list("[M+Na]+", 1, 1, 22.989221080000000086, 16, 1, 1.0, "Na1", "H0"),
        list("[2M+Na]+", 2, 1, 22.989221080000000086, 16, 0, 0.5, "Na1", "H0"),
        list("[3M+Na]+", 3, 1, 22.989221080000000086, 16, 0, 0.25, "Na1", "H0"),
        list("[M+H+Na]2+", 1, 2, 23.996497531999999353, 17, 0, 0.5, "Na1H1", "H0"),
        list("[2M+H+Na]2+", 2, 2, 23.996497531999999353, 17, 0, 0.5, "Na1H1", "H0"),
        list("[3M+H+Na]2+", 3, 2, 23.996497531999999353, 17, 0, 0.25, "Na1H1", "H0"),
        list("[M+2H+Na]3+", 1, 3, 25.003773983999998619, 18, 0, 0.25, "H2Na1", "H0"),
        list("[M+CH3OH+H]+", 1, 1, 33.033491200000000276, 19, 0, 0.25, "C1O1H5", "H0"),
        list("[M-H+Cl]2-", 1, -2, 33.962124838000001148, 20, 0, 1.0, "Cl1", "H1"),
        list("[2M-H+Cl]2-", 2, -2, 33.962124838000001148, 20, 0, 0.5, "Cl1", "H1"),
        list("[3M-H+Cl]2-", 3, -2, 33.962124838000001148, 20, 0, 0.5, "Cl1", "H1"),
        list("[M+Cl]-", 1, -1, 34.969401290000000416, 21, 1, 1.0, "Cl1", "H0"),
        list("[2M+Cl]-", 2, -1, 34.969401290000000416, 21, 0, 0.5, "Cl1", "H0"),
        list("[3M+Cl]-", 3, -1, 34.969401290000000416, 21, 0, 0.5, "Cl1", "H0"),
        list("[M+K-2H]-", 1, -1, 36.948605415999999479, 22, 0, 0.5, "K1", "H2"),
        list("[2M-2H+K]-", 2, -1, 36.948605415999999479, 22, 0, 0.0, "K1", "H2"),
        list("[3M-2H+K]-", 3, -1, 36.948605415999999479, 22, 0, 0.0, "K1", "H2"),
        list("[M+K]+", 1, 1, 38.963158319999998013, 23, 1, 1.0, "K1", "H0"),
        list("[2M+K]+", 2, 1, 38.963158319999998013, 23, 0, 0.5, "K1", "H0"),
        list("[3M+K]+", 3, 1, 38.963158319999998013, 23, 0, 0.25, "K1", "H0"),
        list("[M+H+K]2+", 1, 2, 39.970434771999997281, 24, 0, 0.5, "K1H1", "H0"),
        list("[2M+H+K]2+", 2, 2, 39.970434771999997281, 24, 0, 0.5, "K1H1", "H0"),
        list("[3M+H+K]2+", 3, 2, 39.970434771999997281, 24, 0, 0.25, "K1H1", "H0"),
        list("[M+ACN+H]+", 1, 1, 42.033825557999996646, 25, 0, 0.25, "C2H4N1", "H0"),
        list("[2M+ACN+H]+", 2, 1, 42.033825557999996646, 25, 0, 0.25, "C2H4N1", "H0"),
        list("[M+2Na-H]+", 1, 1, 44.971165708000000902, 26, 0, 0.5, "Na2", "H1"),
        list("[2M+2Na-H]+", 2, 1, 44.971165708000000902, 26, 0, 0.25, "Na2", "H1"),
        list("[3M+2Na-H]+", 3, 1, 44.971165708000000902, 26, 0, 0.25, "Na2", "H1"),
        list("[2M+FA-H]-", 2, -1, 44.998202851999998586, 27, 0, 0.25, "C1O2H2", "H1"),
        list("[M+FA-H]-", 1, -1, 44.998202851999998586, 27, 0, 0.5, "C1O2H2", "H1"),
        list("[M+2Na]2+", 1, 2, 45.978442160000000172, 28, 0, 0.5, "Na2", "H0"),
        list("[2M+2Na]2+", 2, 2, 45.978442160000000172, 28, 0, 0.5, "Na2", "H0"),
        list("[3M+2Na]2+", 3, 2, 45.978442160000000172, 28, 0, 0.25, "Na2", "H0"),
        list("[M+H+2Na]3+", 1, 3, 46.985718611999999438, 29, 0, 0.25, "H1Na2", "H0"),
        list("[M+H+FA]+", 1, 1, 47.012755755999997122, 30, 0, 0.25, "C1O2H3", "H0"),
        list("[M+Hac-H]-", 1, -1, 59.013852915999997607, 31, 0, 0.25, "C2O2H4", "H1"),
        list("[2M+Hac-H]-", 2, -1, 59.013852915999997607, 31, 0, 0.25, "C2O2H4", "H1"),
        list("[M+IsoProp+H]+", 1, 1, 61.064791327999998317, 32, 0, 0.25, "C3H9O1", "H0"),
        list("[M+Na+K]2+", 1, 2, 61.9523793999999981, 33, 0, 0.5, "Na1K1", "H0"),
        list("[2M+Na+K]2+", 2, 2, 61.9523793999999981, 33, 0, 0.5, "Na1K1", "H0"),
        list("[3M+Na+K]2+", 3, 2, 61.9523793999999981, 33, 0, 0.25, "Na1K1", "H0"),
        list("[M+NO3]-", 1, -1, 61.988366450000000895, 34, 0, 0.5, "N1O3", "H0"),
        list("[M+ACN+Na]+", 1, 1, 64.015770185999997464, 35, 0, 0.25, "C2H3N1Na1", "H0"),
        list("[2M+ACN+Na]+", 2, 1, 64.015770185999997464, 35, 0, 0.25, "C2H3N1Na1", "H0"),
        list("[M+NH4+FA]+", 1, 1, 64.039304861999994502, 36, 0, 0.25, "N1C1O2H6", "H0"),
        list("[M-2H+Na+FA]-", 1, -1, 66.980147479999999405, 37, 0, 0.5, "NaC1O2H2", "H2"),
        list("[M+3Na]3+", 1, 3, 68.967663239999993153, 38, 0, 0.25, "Na3", "H0"),
        list("[M+Na+FA]+", 1, 1, 68.99470038399999794, 39, 0, 0.25, "Na1C1O2H2", "H0"),
        list("[M+2Cl]2-", 1, -2, 69.938802580000000832, 40, 0, 1.0, "Cl2", "H0"),
        list("[2M+2Cl]2-", 2, -2, 69.938802580000000832, 40, 0, 0.5, "Cl2", "H0"),
        list("[3M+2Cl]2-", 3, -2, 69.938802580000000832, 40, 0, 0.5, "Cl2", "H0"),
        list("[M+2K-H]+", 1, 1, 76.919040187999996758, 41, 0, 0.5, "K2", "H1"),
        list("[2M+2K-H]+", 2, 1, 76.919040187999996758, 41, 0, 0.25, "K2", "H1"),
        list("[3M+2K-H]+", 3, 1, 76.919040187999996758, 41, 0, 0.25, "K2", "H1"),
        list("[M+2K]2+", 1, 2, 77.926316639999996028, 42, 0, 0.5, "K2", "H0"),
        list("[2M+2K]2+", 2, 2, 77.926316639999996028, 42, 0, 0.5, "K2", "H0"),
        list("[3M+2K]2+", 3, 2, 77.926316639999996028, 42, 0, 0.25, "K2", "H0"),
        list("[M+Br]-", 1, -1, 78.918886479999997619, 43, 1, 1.0, "Br1", "H0"),
        list("[M+Cl+FA]-", 1, -1, 80.974880593999998268, 44, 0, 0.5, "Cl1C1O2H2", "H0"),
        list("[M+AcNa-H]-", 1, -1, 80.995797543999998426, 45, 0, 0.25, "C2H3Na1O2", "H1"),
        list("[M+2ACN+2H]2+", 1, 2, 84.067651115999993292, 46, 0, 0.25, "C4H8N2", "H0"),
        list("[M+K+FA]+", 1, 1, 84.968637623999995868, 47, 0, 0.25, "K1C1O2H2", "H0"),
        list("[M+Cl+Na+FA-H]-", 1, -1, 102.95682522200000619, 48, 0, 0.5, "Cl1Na1C1O2H2", "H1"),
        list("[2M+3H2O+2H]+", 2, 1, 104.03153939599999944, 49, 0, 0.25, "H8O6", "H0"),
        list("[M+TFA-H]-", 1, -1, 112.98558742000000165, 50, 0, 0.5, "C2F3O2H1", "H1"),
        list("[M+H+TFA]+", 1, 1, 115.00014032400000019, 51, 0, 0.25, "C2F3O2H2", "H0"),
        list("[M+3ACN+2H]2+", 1, 2, 125.09420022199999778, 52, 0, 0.25, "C6H11N3", "H0"),
        list("[M+NH4+TFA]+", 1, 1, 132.02668943000000468, 53, 0, 0.25, "N1C2F3O2H5", "H0"),
        list("[M+Na+TFA]+", 1, 1, 136.98208495200000811, 54, 0, 0.25, "Na1C2F3O2H1", "H0"),
        list("[M+Cl+TFA]-", 1, -1, 148.96226516199999423, 55, 0, 0.5, "Cl1C2F3O2H1", "H0"),
        list("[M+K+TFA]+", 1, 1, 152.95602219200000604, 56, 0, 0.25, "K1C2F3O2H1","H0")
    )
    dummy_adduct <- orm$adduct()
    for (adduct in adducts) {
        i <- 0
        dummy_adduct$set_name(adduct[[i <- i + 1]])
        dummy_adduct$set_multi(adduct[[i <- i + 1]])
        dummy_adduct$set_charge(adduct[[i <- i + 1]])
        dummy_adduct$set_mass(adduct[[i <- i + 1]])
        dummy_adduct$set_oidscore(adduct[[i <- i + 1]])
        dummy_adduct$set_quasi(adduct[[i <- i + 1]])
        dummy_adduct$set_ips(adduct[[i <- i + 1]])
        dummy_adduct$set_formula_add(adduct[[i <- i + 1]])
        dummy_adduct$set_formula_ded(adduct[[i <- i + 1]])
        invisible(dummy_adduct$save())
        dummy_adduct$clear(unset_id = TRUE)
    }
    message("Adducts created")
}

insert_base_data <- function(orm, path, archetype = FALSE) {
    if (archetype) {
        ## not implemented yet
        return()
    }
    base_data <- readLines(path)
    for (sql in strsplit(paste(base_data, collapse = " "), ";")[[1]]) {
        orm$execute(sql)
    }
    set_database_version(orm, "enriched")
}

insert_compounds <- function(orm, compounds_path) {
    compounds <- read.csv(file = compounds_path, sep = "\t")
    if (is.null(compounds <- translate_compounds(compounds))) {
        stop("Could not find asked compound's attributes in csv file.")
    }
    dummy_compound <- orm$compound()
    compound_list <- list()
    for (i in seq_len(nrow(compounds))) {
        dummy_compound$set_mz(compounds[i, "mz"])
        dummy_compound$set_name(compounds[i, "name"])
        dummy_compound$set_common_name(compounds[i, "common_name"])
        dummy_compound$set_formula(compounds[i, "formula"])
        compound_list[[length(compound_list) + 1]] <- as.list(
            dummy_compound,
            c("mz", "name", "common_name", "formula")
        )
        dummy_compound$clear(unset_id = TRUE)
    }
    invisible(dummy_compound$save(bulk = compound_list))
}

translate_compounds <- function(compounds) {
    recognized_headers <- list(
        c(
            "HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1",
            "MetName", "ChemFormula", "INChIkey"
        )
    )
    header_translators <- list(
        hmdb_header_translator
    )
    for (index in seq_along(recognized_headers)) {
        headers <- recognized_headers[[index]]
        if (identical(colnames(compounds), headers)) {
            return(header_translators[[index]](compounds))
        }
    }
    if (is.null(translator <- guess_translator(colnames(compounds)))) {
        return(NULL)
    }
    return(csv_header_translator(translator, compounds))
}

guess_translator <- function(header) {
    result <- list(
        # HMDB_ID = NULL,
        mz = NULL,
        name = NULL,
        common_name = NULL,
        formula = NULL,
        # inchi_key = NULL
    )
    asked_cols <- names(result)
    for (asked_col in asked_cols) {
        for (col in header) {
            if ((twisted <- tolower(col)) == asked_col

                next
            }
        }
    }
    if (any(mapply(is.null, result))) {
        return(NULL)
    }
    return(result)
}

hmdb_header_translator <- function(compounds) {
    return(csv_header_translator(
        list(
            HMDB_ID = "HMDB_ID",
            mz = "MzBank",
            name = "MetName",
            common_name = "MetName",
            formula = "ChemFormula",
            inchi_key = "INChIkey"
        ), compounds
    ))
}

csv_header_translator <- function(translation_table, csv) {
    header_names <- names(translation_table)
    result <- data.frame(seq_len(nrow(csv)))
    for (i in seq_along(header_names)) {
        result[, header_names[[i]]] <- csv[, translation_table[[i]]]
    }
    result[, "mz"] <- as.numeric(result[, "mz"])
    return(result)
}

set_database_version <- function(orm, version) {
    orm$set_tag(
        version,
        tag_name = "database_version",
        tag_table_name = "XSeeker_tagging_table"
    )
}

process_rdata <- function(orm, rdata, options) {
    mzml_tmp_dir <- gather_mzml_files(rdata)

        || options$`not-show-percent` == FALSE
    )
    error <- tryCatch({
        process_sample_list(
            orm, rdata, samples,
            show_percent = show_percent,
            file_grouping_var = options$class
        )
        NULL
    }, error = function(e) {
        message(e)
        e
    })
    if (!is.null(mzml_tmp_dir)) {
        unlink(mzml_tmp_dir, recursive = TRUE)
    }
    if (!is.null(error)) {
        stop(error)
    }
}

gather_mzml_files <- function(rdata) {
    if (is.null(rdata$singlefile)) {
        message("Extracting mxml files")
        tmp <- tempdir()
        rdata$singlefile <- utils::unzip(rdata$zipfile, exdir = tmp)
        names(rdata$singlefile) <- tools::file_path_sans_ext(
            basename(rdata$singlefile)
        )
        message("Extracted")
        return(tmp)
    } else {
        message(sprintf(
            "Not a zip file, loading files directly from path: %s",
            paste(rdata$singlefile, collapse = " ; ")
        ))
    }
    return(NULL)
}

process_sample_list <- function(
    orm,
    rdata,
    sample_names,
    show_percent,
    file_grouping_var = NULL
) {
    if (is.null(file_grouping_var)) {
        file_grouping_var <- find_grouping_var(rdata$variableMetadata)
        if (is.null(file_grouping_var)) {
            stop("Malformed variableMetada.")
        }
    }
    tryCatch({
        headers <- colnames(rdata$variableMetadata)
        file_grouping_var <- headers[[as.numeric(file_grouping_var)]]
    }, error = function(e) NULL)
    if (
        is.null(file_grouping_var)
        || !(file_grouping_var %in% colnames(rdata$variableMetadata))
    ) {
        stop(sprintf(
            "Could not find grouping variable %s in var meta file.",
            file_grouping_var
        ))
    }
    message("Processing samples.")
    message(sprintf("File grouping variable: %s", file_grouping_var))

    context <- new.env()
    context$samples <- list()
    context$peaks <- rdata$xa@xcmsSet@peaks
    context$groupidx <- rdata$xa@xcmsSet@groupidx

    process_arg_list <- rdata$listOFlistArguments
    var_meta <- rdata$variableMetadata

    process_params <- list()
    if (is.null(process_arg_list)) {
        for (history in xcms_set@.processHistory) {
            if (
                class(history@param) == "CentWaveParam"
                && history@type == "Peak detection"
            ) {
                params <- history@param
                process_params <- list(list(
                    xfunction = "annotatediff",
                    ppm = params@ppm,
                    peakwidth = sprintf(
                        "%s - %s",
                        params@peakwidth[[1]],
                        params@peakwidth[[2]]
                    ),
                    snthresh = params@snthresh,
                    prefilterStep = params@prefilter[[1]],
                    prefilterLevel = params@prefilter[[2]],
                    mzdiff = params@mzdiff,
                    fitgauss = params@fitgauss,
                    noise = params@noise,
                    mzCenterFun = params@mzCenterFun,
                    integrate = params@integrate,
                    firstBaselineCheck = params@firstBaselineCheck,
                    snthreshIsoROIs = !identical(params@roiScales, numeric(0))
                ))
                break
            }
        }
    } else {
        for (list_name in names(process_arg_list)) {
            param_list <- list()
            for (param_name in names(process_arg_list[[list_name]])) {
                param_list[[param_name]] <- process_arg_list[[
                    list_name
                ]][[param_name]]
            }
            process_params[[length(process_params) + 1]] <- param_list
        }
    }

    message("Parameters from previous processes extracted.")


    indices <- as.numeric(unique(var_meta[, file_grouping_var]))
    if (any(is.null(names(singlefile)[indices]))) {
        stop(sprintf(
            paste(
                "Indices defined by grouping variable %s are not all present",
                "in singlefile names (%s).\nCannot continue. Indices: %s"
            ),
            file_grouping_var,
            paste(names(singlefile), collapse = ", "),
            paste(indices, collapse = ", ")
        ))
    }
    smol_xcms_set <- orm$smol_xcms_set()
    mz_tab_info <- new.env()
    g <- xcms::groups(xcms_set)
    mz_tab_info$group_length <- nrow(g)
    mz_tab_info$dataset_path <- xcms::filepaths(xcms_set)
    mz_tab_info$sampnames <- xcms::sampnames(xcms_set)
    mz_tab_info$sampclass <- xcms::sampclass(xcms_set)
    mz_tab_info$rtmed <- g[, "rtmed"]
    mz_tab_info$mzmed <- g[, "mzmed"]
    mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(
        xcms_set,
        value = "into"
    )
    blogified <- blob::blob(fst::compress_fst(
        serialize(mz_tab_info, NULL),
        compression = 100
    ))
    rm(mz_tab_info)

    invisible(smol_xcms_set$set_raw(blogified)$save())
    smol_xcms_set_id <- smol_xcms_set$get_id()
    rm(smol_xcms_set)

        env$enriched_rdata_version <- ENRICHED_RDATA_VERSION
        env$tool_name <- TOOL_NAME
        env$enriched_rdata_doc <- ENRICHED_RDATA_DOC

        sample <- add_sample_to_database(orm, env, context, smol_xcms_set_id)
        rm(env)
        context$samples[no] <- sample$get_id()
        rm(sample)
    }
    context$clusters <- list()
    context$show_percent <- show_percent
    context$cluster_mean_rt_abundance <- list()
    context$central_feature <- list()

    clusters <- context$clusters
    rm(context)
    message("Features enrichment")
    complete_features(orm, clusters, show_percent)
    message("Features enrichment done.")
    return(NULL)
}

find_grouping_var <- function(var_meta) {
    known_colnames <- c(
        "name", "namecustom", "mz", "mzmin", "mzmax",
        "rt", "rtmin", "rtmax", "npeaks", "isotopes", "adduct",
        "pcgroup", "ms_level"
    )
    col_names <- colnames(var_meta)
    classes <- list()
    for (name in col_names) {
        if (!(name %in% known_colnames)) {
            classes[[length(classes) + 1]] <- name
        }
    }
    if (length(classes) > 1) {
        stop(sprintf(
            "Only one class expected in the variable metadata. Found %d .",
            length(classes)
        ))
    }
    if (length(classes) == 0) {
        stop("Could not find any class column in your variableMetadata.")
    }
    return(classes[[1]])
}

add_sample_to_database <- function(orm, env, context, smol_xcms_set_id) {
    message(sprintf("Processing sample %s", env$sample_name))
    sample <- (
        orm$sample()
        $set_name(env$sample_name)
        $set_path(env$dataset_path)
        $set_kind("enriched_rdata")
        $set_polarity(
            if (
                is.null(env$polarity)
                || identical(env$polarity, character(0))
            ) ""
            else env$polarity
        )
        $set_raw(blob::blob(fst::compress_fst(
            serialize(env, NULL),
            compression = 100
        )))
    )
    sample[["smol_xcms_set_id"]] <- smol_xcms_set_id
    sample$modified__[["smol_xcms_set_id"]] <- smol_xcms_set_id
    sample <- sample$save()
    load_process_params(orm, sample, env$process_params)
    message(sprintf("Sample %s inserted.", env$sample_name))
    return(sample)
}


load_variable_metadata <- function(orm, var_meta, context) {
    all_clusters <- orm$cluster()$all()

        orm, var_meta, context,
        next_feature_id, next_cluster_id,
        next_pc_group, next_align_group
    ))
    message("Extracting features done.")
    return(NULL)
}

get_next_id <- function(models, attribute) {
    if ((id <- models$max(attribute)) == Inf || id == -Inf) {
        return(0)
    }
    return(id)
}

create_features <- function(
    orm, var_meta, context,
    next_feature_id, next_cluster_id,
    next_pc_group, next_align_group
) {
    field_names <- as.list(names(orm$feature()$fields__))
    field_names[field_names == "id"] <- NULL

    features <- list()
    dummy_feature <- orm$feature()

    if (show_percent <- context$show_percent) {

        total <- nrow(var_meta)
    }
    rows <- seq_len(nrow(var_meta))
    if (PROCESS_SMOL_BATCH) {

        rows <- rows[1:as.integer(FAST_FEATURE_RATIO / 100.0 * length(rows))]
    }
    cluster_row <- list()
    for (row in rows) {
        if (show_percent && (row / total) * 100 > percent) {
            percent <- percent + 1
            message("\r", sprintf("\r%d %%", percent), appendLF = FALSE)
        }

        dummy_feature$set_featureID(next_feature_id)
        next_feature_id <- next_feature_id + 1

        context$clusterID <- clusterID
        dummy_feature$set_iso(iso)

        peak_list <- context$peaks[context$groupidx[[row]], ]
        if (! ("matrix" %in% class(peak_list))) {
            peak_list <- matrix(
                peak_list,
                nrow = 1,
                ncol = length(peak_list),
                dimnames = list(c(), names(peak_list))
            )
        }

        clusterID <- as.character(clusterID)
        if (is.null(context$central_feature[[clusterID]])) {
            int_o <- extract_peak_var(peak_list, "into")
            context$central_feature[[clusterID]] <- (
                peak_list[peak_list[, "into"] == int_o, ]["sample"]
            )
        }

        if (!DEBUG_FAST_IGNORE_SLOW_OP) {
            central_feature <- context$central_feature[[clusterID]]
            sample_peak_list <- peak_list[
                as.integer(peak_list[, "sample"]) == central_feature,
                ,
                drop = FALSE
            ]
            if (
                !identical(sample_peak_list, numeric(0))
                && !is.null(nrow(sample_peak_list))
                && nrow(sample_peak_list) != 0
            ) {
                int_o <- extract_peak_var(sample_peak_list, "into")
                if (!is.na(int_o)) {
                    dummy_feature$set_int_o(int_o)
                }
                int_b <- extract_peak_var(sample_peak_list, "intb")
                if (!is.na(int_b)) {
                    dummy_feature$set_int_b(int_b)
                }
                max_o <- extract_peak_var(sample_peak_list, "maxo")
                if (!is.na(max_o)) {
                    dummy_feature$set_max_o(max_o)
                }
            }
        }

            dummy_feature, clusterID,
            context, curent_var_meta, next_pc_group,
            next_align_group
        )
        next_align_group <- next_align_group + 1
        features[[length(features) + 1]] <- as.list(dummy_feature, field_names)
        dummy_feature$clear()
    }
    rm(var_meta)
    message("")
    message("Saving features")
    invisible(dummy_feature$save(bulk = features))

    ## We link manually clusters to the sample they're in.
    link_cache <- list()
    for (row in rows) {
        sample_nos <- unique(context$peaks[context$groupidx[[row]], "sample"])
        for (sample_id in context$samples[sample_nos]) {
            cluster_id <- cluster_row[[row]]$get_id()
            id <- paste(sample_id, cluster_id, sep = ";")
            if (is.null(link_cache[[id]])) {
                link_cache[[id]] <- 1
                orm$cluster_sample(
                    sample_id = sample_id,
                    cluster_id = cluster_id
                )$save()
            }
        }
    }

    message("Saved.")
    return(context$clusters)
}

extract_peak_var <- function(peak_list, var_name, selector = max) {
    value <- peak_list[, var_name]
    names(value) <- NULL
    return(selector(value))
}

set_feature_fields_from_var_meta <- function(feature, var_meta) {
    if (!is.null(mz <- var_meta[["mz"]]) && !is.na(mz)) {
        feature$set_mz(mz)

        feature$set_rt_max(rtmax)
    }
    if (!is.null(isotopes <- var_meta[["isotopes"]]) && !is.na(isotopes)) {
        feature$set_iso(isotopes)
    }
    return(feature)
}

extract_iso  <- function(weird_data) {
    if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
        return(sub("^\\[\\d+\\]", "", weird_data, perl = TRUE))
    }
    return(weird_data)
}

extract_clusterID <- function(weird_data, next_cluster_id) {
    if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
        clusterID <- stringr::str_extract(weird_data, "^\\[\\d+\\]")
        clusterID <- as.numeric(stringr::str_extract(clusterID, "\\d+"))
    } else {
        clusterID <- 0
    }
    return(clusterID + next_cluster_id)
}

create_associated_cluster <- function(
    orm,
    main_sample_id, feature, clusterID,

    if (is.null(cluster <- context$clusters[[clusterID]])) {
        pcgroup <- as.numeric(curent_var_meta[["pcgroup"]])
        adduct_name <- as.character(curent_var_meta[["adduct"]])
        annotation <- curent_var_meta[["isotopes"]]
        cluster <- context$clusters[[clusterID]] <- orm$cluster(
            pc_group = pcgroup + next_pc_group,
            # adduct=adduct,
            align_group = next_align_group,
            # curent_group=curent_group,
            clusterID = context$clusterID,
            annotation = annotation
        )
        if (is.null(adduct <- context$adducts[[adduct_name]])) {
            context$adducts[[adduct_name]] <- orm$adduct()$load_by(
                name = adduct_name
            )$first()
            if (is.null(adduct <- context$adducts[[adduct_name]])) {
                adduct <- context$adducts[[adduct_name]] <- orm$adduct(
                    name = adduct_name,
                    charge = 0
                )
                adduct$save()
            }
        }
        cluster$set_adduct(adduct)
        ## Crappy hack to assign sample id to cluster without loading the
        ## sample. Samples are too big (their sample$env) and slows the
        ## process, and eat all the menory so we dont't want to load them.
        cluster[["sample_id"]] <- main_sample_id
        cluster$modified__[["sample_id"]] <- main_sample_id
    } else {
        if (context$clusterID != 0 && cluster$get_clusterID() == 0) {
            cluster$set_clusterID(context$clusterID)
        }
    }
    cluster$save()
    feature$set_cluster(cluster)
    return(cluster)
}

complete_features <- function(orm, clusters, show_percent) {
    total <- length(clusters)
    percent <- -1
    i <- 0
    for (cluster in clusters) {
        i <- i + 1
        if (show_percent && (i / total) * 100 > percent) {
            percent <- percent + 1
            message("\r", sprintf("\r%d %%", percent), appendLF = FALSE)
        }
        features <- orm$feature()$load_by(cluster_id = cluster$get_id())
        if (features$any()) {
            if (!is.null(rt <- features$mean("rt"))) {
                cluster$set_mean_rt(rt)$save()
            }
            features_df <- as.data.frame(features)
            central_feature <- features_df[
                grepl("^\\[M\\]", features_df[, "iso"]),
            ]
            central_feature_into <- central_feature[["int_o"]]
            if (
                !identical(central_feature_into, numeric(0))
                && central_feature_into != 0
            ) {
                for (feature in as.vector(features)) {
                    feature$set_abundance(
                        feature$get_int_o() / central_feature_into * 100
                    )$save()
                }
            }
        }
    }
    return(NULL)
}

load_process_params <- function(orm, sample, params) {
    for (param_list in params) {
        if (is.null(param_list[["xfunction"]])) {

        }
        if (param_list[["xfunction"]] == "annotatediff") {
            load_process_params_peak_picking(orm, sample, param_list)
        }
    }
    return(sample)
}

load_process_params_peak_picking <- function(
    orm,
    sample,
    peak_picking_params
) {
    return(add_sample_process_parameters(
        params = peak_picking_params,
        params_translation = list(
            ppm = "ppm",
            maxcharge = "maxCharge",
            maxiso = "maxIso"
        ),
        param_model_generator = orm$peak_picking_parameters,
        sample_param_setter = sample$set_peak_picking_parameters
    ))
}

add_sample_process_parameters <- function(
    params,

        params_model <- params_models$first()
    } else {
        params_model <- do.call(param_model_generator, model_params)
        params_model$save()
    }
    return(sample_param_setter(params_model)$save())
}


library(optparse)

option_list <- list(
    optparse::make_option(
        c("-v", "--version"),
        action = "store_true",
        help = "Display this tool's version and exits"
    ),
    optparse::make_option(
        c("-i", "--input"),
        type = "character",
        help = "The rdata path to import in XSeeker"
    ),
    optparse::make_option(
        c("-s", "--samples"),
        type = "character",
        help = "Samples to visualise in XSeeker"
    ),
    optparse::make_option(
        c("-B", "--archetype"),
        type = "character",
        help = "The name of the base database"
    ),
    optparse::make_option(
        c("-b", "--database"),
        type = "character",
        help = "The base database's path"
    ),
    optparse::make_option(
        c("-c", "--compounds-csv"),
        type = "character",
        help = "The csv containing compounds"
    ),
    optparse::make_option(
        c("-m", "--models"),
        type = "character",
        help = paste(
            "The path or url (must begin with http[s]:// or git@) to",
            "the database's models"
        )
    ),
    optparse::make_option(
        c("-k", "--class"),
        type = "character",
        help = "The name of the column containing the classes" 
   ),
    optparse::make_option(
        c("-o", "--output"),
        type = "character",
        help = "The path where to output sqlite"
    ),
    optparse::make_option(
        c("-P", "--not-show-percent"),
        action = "store_true",
        help = "Flag not to show the percents",
        default = FALSE
    )
)

options(error = function(){traceback(3)})

parser <- OptionParser(
    usage = "%prog [options] file",
    option_list = option_list
)
args <- parse_args(parser, positional_arguments = 0)

err_code <- 0

if (!is.null(args$options$version)) {
    message(sprintf("%s %s", TOOL_NAME, VERSION))
    quit()
}

models <- get_models(args$options$models)
orm <- DBModelR::ORM(
    connection_params = list(dbname=args$options$output),
    dbms = "SQLite"
)

invisible(orm$models(models))
invisible(create_database(orm))

    insert_base_data(orm, args$options$database)
}
message(sprintf("Base data inserted using %s.", args$options$database))

if (!is.null(args$options$archetype)) {
    insert_base_data(orm, args$options$archetype, archetype = TRUE)
}
if (!is.null(args$options$`compounds-csv`)) {
    insert_compounds(orm, args$options$`compounds-csv`)
}

load(args$options$input, rdata <- new.env())

process_rdata(orm, rdata, args$options)

quit(status = err_code)