<tool id="palom" name="palom" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>Piecewise alignment for layers of image mosaics.</description>
    
    <macros>
        <import>macros.xml</import>
    </macros>
    
    <expand macro="requirements" />
    
    <command detect_errors="exit_code"><![CDATA[
        
        ln -s '$reference_image' './ref.svs' &&

        ln -s '$channels' './channels.csv' &&

        #for $i in $moving_images:
            ln -s '$i' '${i.name}' &&
        #end for

        python '$script'

    ]]></command>
    <configfiles>
        <configfile name = "script">
import os
import pathlib
import pandas as pd
import matplotlib.pyplot as plt 
import palom
from palom.cli.svs import run_palom

plt.switch_backend('agg')

cwd = os.getcwd()

ref_img = pathlib.Path(os.path.join(cwd,'ref.svs'))

images = [ref_img]
channels = ['hematoxylin']
modes = ['hematoxylin']

#for $i in $moving_images:
img = pathlib.Path('${i.name}')
images.append(img)
#end for

channel_df = pd.read_csv(os.path.join(cwd, 'channels.csv'))
channel_df = channel_df.astype({'channel':'string'})
channels += list(channel_df['channel'])
modes += list(channel_df['mode'])

pixel_size = None

#if $pixel_size:
pixel_size = float($pixel_size)
#end if

level = int($level)
downscale_factor = int($downscale_factor)

output_path = pathlib.Path(os.path.join(cwd, 'registered.ome.tiff'))

qc_path = output_path.parent / 'qc'

os.mkdir(qc_path)

run_palom(
    img_paths = images,
    img_modes = modes,
    pixel_size = pixel_size,
    channel_names = channels,
    output_path = output_path,
    qc_path = qc_path,
    level = level,
    downscale_factor = downscale_factor
)
        </configfile>
    </configfiles>
    <inputs>
        <param name="reference_image" type="data" format="tiff" label="Hematoxylin reference image" help=".svs format image to align to"/>
        <param name="moving_images" type="data_collection" label="Moving Images (data collection)" help="A collection of .svs format images to align to the reference image"/>
        <param name="channels" type="data" label="Channel and mode dataframe" help="A CSV file with 'channel' and 'mode' columns, with a row for each moving image"/>
        <param name="pixel_size" type="float" optional="true" label="Pixel size (optional)" help="defaults to px size of reference image"/>
        <param name="level" type="integer" value="0" label="Pyramid level" help="pyramid level, defaults to 0 (lowest level)"/>
        <param name="downscale_factor" type="integer" value="2" label="Pyramid downscale factor" help="Default of 2 makes pyramid downscaling compatible with Avivator and Vitessce"/>
    </inputs>
    <outputs>
        <collection name="qc_plots" type="list" label="${tool.name}: qc_plots">
            <discover_datasets pattern="__name_and_ext__" directory="qc" ext="png" visible="false"/>
        </collection>
        <data name="registered" from_work_dir="registered.ome.tiff" format="ome.tiff"/>
    </outputs>
    <tests>
        <test>
            <param name="reference_image" value="test_ref.svs"/>
            <param name="moving_images">
                <collection type="list">
                    <element name="m1" value="test_moving.svs"/>
                </collection>
            </param>
            <param name="channels" value="test_channels.csv"/>
            <param name="level" value="1"/>
            <output_collection name="qc_plots" type="list">
                <element name="01-test_moving.svs" ftype="png">
                    <assert_contents>
                        <has_size value="277000" delta="100000" />
                    </assert_contents>
                </element>
            </output_collection>
            <output name="registered" ftype="ome.tiff">
                <assert_contents>
                    <has_size value="59000000" delta="20000000" />
                </assert_contents>
            </output>
        </test>
    </tests>
    <help><![CDATA[
------
PALOM
------
**PALOM** performs registration of whole-slide images with different IHC stainings.
**Inputs**
1.) Reference image for aligning image mosiacs to
2.) A collection of images to be aligned
3.) File describing the channels/targets

**Outputs**
1.) Registered image as ome.tiff
2.) Colleciton of QC plots

More details on Github: https://github.com/Yu-AnChen/palom

    ]]></help>
    <expand macro="citations" />
</tool>