<tool id="aplcms_unsupervised" name="apLCMS - Unsupervised" version="@TOOL_VERSION@+galaxy2">
    <macros>
        <import>aplcms_macros.xml</import>
    </macros>

    <expand macro="requirements" />

    <command detect_errors="aggressive"><![CDATA[
        #set file_str = str("', '").join([str($f) for $f in $files])

        Rscript
            -e "x <- apLCMS::unsupervised(
                    files = c('$file_str'),
                    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 = $weak_signal_recovery.recover_min_count,
                    cluster = as.integer(\${GALAXY_SLOTS:-1})
                )"
            -e "apLCMS::save_peaks_to_hdf('$peaks', x)"
    ]]></command>

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

    <outputs>
        <data name="peaks" format="h5" />
    </outputs>

    <tests>
        <test>
            <param name="files" value="mbr_test0.mzml,mbr_test1.mzml,mbr_test2.mzml" ftype="mzml"/>
            <output name="peaks" file="peaks_unsupervised.h5" ftype="h5" 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>