<tool id="biomd_rmsd_clustering" name="Hierarchical clustering" version="0.@TOOL_VERSION@+galaxy@GALAXY_VERSION@">
    <description>from MD RMSD matrix data</description>
    <macros>
        <token name="@TOOL_VERSION@">1.5.2</token>
        <token name="@GALAXY_VERSION@">0</token>
    </macros>
    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">scipy</requirement>
        <requirement type="package" version="1.19.1">numpy</requirement>
        <requirement type="package" version="3.3.1">matplotlib</requirement>
    </requirements>
    <command detect_errors="aggressive"><![CDATA[
        python '$__tool_directory__/rmsd_clustering.py'
            #if $inp.ext == 'json':
                --json '$inp'
                --outp-mat '$outp_mat'
            #elif $inp.ext == 'tabular':
                --mat '$inp'
            #end if
            --Z '$Z'
            #if $dendrogram:
                --dendrogram '$dend'
                --clustering-method '$clustering_method'
            #end if
            #if $heatmap:
                --heatmap '$hmap'
                --cmap '$cmap'
            #end if
            --start '$start'
            --end '$end'
            '$normalize'
]]></command>
    <inputs>
        <param label="JSON or tabular input file" type="data" format="json,tabular" name="inp" argument="--json"/>
        <param label="First trajectory frame to calculate distance matrix" value="0" type="integer" name="start" argument="--start"/>
        <param label="Last trajectory frame to calculate distance matrix" value="-1" type="integer" name="end" argument="--end"/>
        <param label="Normalize the RMSD variation over the trajectories before averaging." checked="false" type="boolean" name="normalize" argument="--normalize" truevalue="--normalize" falsevalue=""/>
        <param label="Output dendrogram?" type="boolean" name="dendrogram" argument="--dendrogram" />
        <param label="Output distance matrix file?" type="boolean" name="heatmap" argument="--heatmap"/>
        <param label="Method to use for clustering." type="select" name="clustering_method" argument="--clustering-method">
            <option selected="true" value="average">average</option>
            <option value="centroid">centroid</option>
            <option value="complete">complete</option>
            <option value="median">median</option>
            <option value="single">single</option>
            <option value="ward">ward</option>
            <option value="weighted">weighted</option>
        </param>
        <param label="Matplotlib colormap to use for plotting distance matrix." value="plasma" type="text" name="cmap" argument="--cmap"/>
    </inputs>
    <outputs>
        <data label="Tabular output file" format="tabular" name="outp_mat">
            <filter>inp.ext == 'json'</filter>
        </data>
        <data label="Dendrogram" format="png" name="dend">
            <filter>dendrogram</filter>
        </data>
        <data label="Heatmap" format="png" name="hmap">
            <filter>heatmap</filter>
        </data>
        <data label="File for cluster linkage array" format="tabular" name="Z"/>
    </outputs>
    <tests>
        <test expect_num_outputs="4">
            <param name="inp" value="inp.json"/>
            <param name="dendrogram" value="true"/>
            <param name="heatmap" value="true"/>
            <param name="clustering_method" value="average"/>
            <param name="cmap" value="plasma"/>
            <param name="start" value="0"/>
            <param name="end" value="-1"/>
            <param name="normalize" value="true"/>
            <output name="outp_mat" value="outp_mat.tabular"/>
            <output name="dend" value="dendrogram.png"/>
            <output name="hmap" value="heatmap.png"/>
            <output name="Z" value="Z.tabular"/>
        </test>
    </tests>
    <help><![CDATA[
.. class:: infomark

**What it does**

This tool takes the three-dimensional tensor file (in JSON format) produced by the 'Extract RMSD distance matrix data' tool and flattens it along the time axix to give a two-dimensional distance matrix.

Optionally, it also plots the distance matrix as a heatmap with matplotlib, performs hierarchical clustering with scipy, and plots the corresponding dendrogram.

_____


.. class:: infomark

**Input**

       - Three-dimensional tensor (JSON).
       - User selection of desired outputs, clustering method and other parameters

_____


.. class:: infomark

**Output**

       - Tabular file containing a two-dimensional N x N distance matrix, where N is the number of MD trajectories
       - Optional: a heatmap representing the distance matrix.
       - Optional: a tabular file containing the cluster linkage array produced by hierarchical clustering of the distance matrix
       - Optional: A dendrogram representing the hierarchical clustering.
    ]]></help>
    <citations>
        <citation type="doi">10.1038/s41592-019-0686-2</citation>
        <citation type="doi">{10.1109/MCSE.2007.55</citation>
    </citations>
</tool>