<tool id="_recetox_aplcms_unsupervised" name="apLCMS Unsupervised" version="@TOOL_VERSION@+galaxy0">
    <description>generate a feature table from LC/MS spectra</description>
    <macros>
        <import>recetox_aplcms_macros.xml</import>
    </macros>

    <expand macro="requirements" />

    <command detect_errors="aggressive"><![CDATA[
        sh ${symlink_inputs} &&
        Rscript  -e 'source("${__tool_directory__}/main.R")' -e 'source("${run_script}")'
    ]]></command>

    <configfiles>
        <configfile name="symlink_inputs">
            #for $infile in $files
                ln -s '${infile}' '${infile.element_identifier}'
            #end for
        </configfile>
        <configfile name="run_script"><![CDATA[
            #set filenames_str = str("', '").join([str($f.element_identifier) for $f in $files])

            unsupervised_main(
                sample_files = c('$filenames_str'),
                aligned_file = '${aligned_feature_sample_table}',
                recovered_file = '${recovered_feature_sample_table}',
                min_exp = $noise_filtering.min_exp,
                min_pres = $noise_filtering.min_pres,
                min_run = $noise_filtering.min_run,
                mz_tol = $noise_filtering.mz_tol,
                baseline_correct = $noise_filtering.baseline_correct,
                baseline_correct_noise_percentile = $noise_filtering.baseline_correct_noise_percentile,
                intensity_weighted = $noise_filtering.intensity_weighted,
                shape_model = '$feature_detection.shape_model',
                BIC_factor = $feature_detection.BIC_factor,
                peak_estim_method = '$feature_detection.peak_estim_method',
                min_bandwidth = $feature_detection.min_bandwidth,
                max_bandwidth = $feature_detection.max_bandwidth,
                sd_cut = c($feature_detection.sd_cut_min, $feature_detection.sd_cut_max),
                sigma_ratio_lim = c($feature_detection.sigma_ratio_lim_min, $feature_detection.sigma_ratio_lim_max),
                component_eliminate = $feature_detection.component_eliminate,
                moment_power = $feature_detection.moment_power,
                align_chr_tol = $peak_alignment.align_chr_tol,
                align_mz_tol = $peak_alignment.align_mz_tol,
                max_align_mz_diff = $peak_alignment.max_align_mz_diff,
                recover_mz_range = $weak_signal_recovery.recover_mz_range,
                recover_chr_range = $weak_signal_recovery.recover_chr_range,
                use_observed_range = $weak_signal_recovery.use_observed_range,
                recover_min_count = $weak_signal_recovery.recover_min_count,
                cluster = as.integer(Sys.getenv('GALAXY_SLOTS', unset = 1))
            )
        ]]></configfile>
    </configfiles>

    <expand macro="inputs">
        <expand macro="noise_filtering" />
        <expand macro="feature_detection" />
        <expand macro="peak_alignment" />
        <expand macro="weak_signal_recovery" />
    </expand>

    <outputs>
        <expand macro="unsupervised_outputs" />
    </outputs>

    <tests>
        <test>
            <param name="files" value="mbr_test0.mzml,mbr_test1.mzml,mbr_test2.mzml" ftype="mzml" />
            <output name="recovered_feature_sample_table" file="unsupervised_recovered_feature_sample_table.parquet" ftype="parquet" compare="sim_size" delta="1000" />
        </test>
    </tests>

    <help>
        This is the Unsupervised version of apLCMS which is not relying on any existing knowledge about metabolites or
        any historically detected features. For such functionality please use the Hybrid version of apLCMS.

        @GENERAL_HELP@
    </help>

    <expand macro="citations" />
</tool>