view points_association_nn.xml @ 0:04e692ee53a8 draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/points_association_nn/ commit db4c2a87a21f32e5d12d11e68f32773bfc06fcfd"
author imgteam
date Thu, 22 Jul 2021 22:29:47 +0000
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children fd4293bee0dc
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<tool id="ip_points_association_nn" name="Association of points" version="0.0.1" profile="20.05"> 
    <description>in consecutive frames (slices) using the nearest neighbor algorithm</description>
    <requirements>
        <requirement type="package" version="1.20.2">numpy</requirement>
        <requirement type="package" version="3.0.7">openpyxl</requirement>
        <requirement type="package" version="1.2.4">pandas</requirement>
        <requirement type="package" version="0.18.1">scikit-image</requirement>
    </requirements>
    <command>
    <![CDATA[
    python '$__tool_directory__/points_association_nn.py'
         '$fn_in'
         ./output.xlsx
         '$nbpx'
         '$thres'
         '$minlen'
    ]]>
    </command>
    <inputs>
        <param name="fn_in" type="data" format="tabular" label="Name of input file (tsv tabular)" />
        <param name="nbpx" type="integer" value="6" label="Neighborhood size in pixel" />
        <param name="thres" type="float" value="25" label="Percentage (%) of the global maximal intensity for thresholding some event" />
        <param name="minlen" type="float" value="50" label="Minimum length of tracks (% of sequence length)" />
    </inputs>
    <outputs>
        <data format="xlsx" name="fn_out" from_work_dir="output.xlsx" />
    </outputs>
    <tests>
        <test>
            <param name="fn_in" value="spots_detected.tsv"/>
            <param name="nbpx" value="6"/>
            <param name="thres" value="25"/>
            <param name="minlen" value="50"/>
            <output name="fn_out" value="spots_linked.xlsx" ftype="xlsx" compare="sim_size"/>
        </test>
    </tests>
    <help>
    **What it does**

    This tool associates points in consecutive frames (slices) using the nearest neighbor algorithm.
    </help>
</tool>