<tool id="unmicst" name="UnMicst" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01">
    <description>Image segmentation - probability map generation</description>

    <macros>
        <import>macros.xml</import>
    </macros>
 
    <expand macro="requirements"/>
    <expand macro="version_cmd"/>

    <command detect_errors="exit_code"><![CDATA[
    #set $ext = str($image.file_ext)
    #if $ext == 'tiff'
        #set $ext = 'tif'
    #end if
    #set $input = 'image.' + str($ext) 

    ln -s '$image' '$input' &&
    
    @CMD_BEGIN@ '$input'

    --tool $tool
    
    #if $stackoutput
        --stackOutput
    #end if

    --outputPath '.'
    --channel $channel
    --model $model
    --mean $mean
    --std $stdev
    --scalingFactor $scalingfactor
    --outlier $outlier &&

    ## Move files to different files for from_work_dir differentiation
    #if $stackoutput
        mv *Probabilities*.tif Probabilities.tif &&
        mv qc/*Preview*.tif Preview.tif
    #else
        mv *ContoursPM*.tif ContoursPM.tif &&
        mv *NucleiPM*.tif NucleiPM.tif 
    #end if
    ]]></command>

    <inputs>
        <param name="tool" type="select" label="UnMicst Tool">
            <option selected="true" value="unmicst-solo">unmicst-solo</option>
            <option value="unmicst-duo">unmicst-duo</option>
            <option value="unmicst-legacy">unmicst-legacy</option>
            <option value="UnMicstCyto2">UnMicstCyto2</option>
        </param>
        <param name="image" type="data" format="tiff" label="Registered TIFF"/>
        <param name="model" type="select" label="Model">
            <option value="nucleiDAPI">nucleiDAPI</option>
            <option value="CytoplasmIncell">CytoplasmIncell</option>
            <option value="CytoplasmIncell2">CytoplasmIncell2</option>
            <option value="CytoplasmZeissNikon">CytoplasmZeissNikon</option>
            <option value="mousenucleiDAPI">mousenucleiDAPI</option>
            <option value="nucleiDAPI1-5">nucleiDAPI1-5</option>
            <option value="nucleiDAPILAMIN">nucleiDAPILAMIN</option>
        </param>
        <param name="mean" type="float" value="-1" label="Mean (-1 for model default)"/>
        <param name="stdev" type="float" value="-1" label="Standard Deviation (-1 for model default)"/>
        <param name="channel" type="integer" value="0" label="Channel to perform inference on"/>
        <param name="stackoutput" type="boolean"  label="Stack probability map outputs"/>
        <param name="scalingfactor" type="float" value="1.0" label="Factor to scale by"/>
        <param name="outlier" type="float" value="-1.0" label="Map percentile intensity to max when rescaling intensity values"/>
    </inputs>

    <outputs>
        <data format="tiff" name="previews" from_work_dir="Preview.tif" label="${tool.name} on ${on_string}: Preview">
            <filter>stackoutput</filter>
        </data>
        <data format="tiff" name="probabilities" from_work_dir="Probabilities.tif" label="${tool.name} on ${on_string}: Probabilities">
            <filter>stackoutput</filter>
        </data>
        <data format="tiff" name="contours" from_work_dir="ContoursPM.tif" label="${tool.name} on ${on_string}: ContoursPM">
            <filter>not stackoutput</filter>
        </data>
        <data format="tiff" name="nuclei" from_work_dir="NucleiPM.tif" label="${tool.name} on ${on_string}: NucleiPM">
            <filter>not stackoutput</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="2">
            <param name="image" value="105.tif" ftype="tiff" />
            <param name="model" value="nucleiDAPI" />
            <param name="tool" value="unmicst-legacy"/>
            <param name="channel" value="1"/>
            <output name="nuclei" file="105_NucleiPM_1.tif" compare="sim_size" delta="10" />
            <output name="contours" file="105_ContoursPM_1.tif" compare="sim_size" delta="10" />
        </test>
    </tests>
    <help><![CDATA[
-------
UNMICST
-------

**UnMICST** uses a convolutional neural network to annotate each pixel with the probability that it belongs to a given subcellular component (nucleus, cytoplasm, cell boundary). Check the UnMICST website for the most up-to-date documentation.
**Input**
An .ome.tif, preferably flat field corrected. The model is trained on images acquired at a pixelsize of 0.65 microns/px. If your settings differ, you can upsample/downsample to some extent.
**Output**
1. a .tif stack where the different probability maps for each class are concatenated in the Z-axis in the order: nuclei foreground, nuclei contours, and background.
2. a QC image with the DNA image concatenated with the nuclei contour probability map with suffix Preview
More infortion available at: https://labsyspharm.github.io/UnMICST-info/.
    ]]></help>
    <expand macro="citations" />
</tool>