annotate 3d_tensor_feature_dimension_reduction.xml @ 0:e8f64a98cbc6 draft default tip

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/3d_tensor_feature_dimension_reduction/ commit e82400162e337b36c29d6e79fb2deb9871475397"

<tool id="ip_3d_tensor_feature_dimension_reduction" name="Dimensionality Reduction" version="0.0.1" profile="20.05"> 
    <description>for features of 3D tensor data using UMAP</description>
    <requirements>
        <requirement type="package" version="1.20.2">numpy</requirement>
        <requirement type="package" version="3.6.0">h5py</requirement>
        <requirement type="package" version="2020.10.1">tifffile</requirement>
        <requirement type="package" version="0.5.2">umap-learn</requirement>
    </requirements>
    <command detect_errors="aggressive"><![CDATA[
        ln -s '$fn_in' ./input.h5 &&
        python '$__tool_directory__/3d_tensor_feature_dimension_reduction.py'
        ./input.h5
        '$nCh'
        ./output.tif
    ]]>
    </command>
    <inputs>
        <param name="fn_in" type="data" format="hdf5" label="3D tensor data (rows x cols x features)" />
        <param name="nCh" type="integer" value="5" optional="true" min="1" max="10" label="Reduced dimensions of features" />
    </inputs>
    <outputs>
        <data format="tiff" name="fn_out" from_work_dir="output.tif" />
    </outputs>
    <tests>
        <test>
            <param name="fn_in" value="tensor.h5"/>
            <param name="nCh" value="3"/>
            <output name="fn_out" value="tensor_r.tif" ftype="tiff" compare="sim_size"/>
        </test>
    </tests>
    <help>
    **What it does**

    This tool performs dimensionality reduction for features of 3D tensor data (rows x cols x features) using UMAP. The results will be saved as a multi-channel 32-bit TIFF image (features x rows x cols).
    </help>
    <citations>
    </citations>
</tool>