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Merge pull request #9559 from astrovsky01/IT_migrate

Browse Source 
It migrate from EU
This commit is contained in:
John Chilton
2020-04-20 14:27:58 -04:00
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parent 592f8fdbf1 2641c692e2
commit 77e7b544c7
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tools/interactive/interactivetool_climate_notebook.xml
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<tool id="interactive_tool_climate_notebook" tool_type="interactive" name="Interactive Climate Notebook" version="0.2">
<requirements>
<container type="docker">nordicesmhub/docker-climate-notebook:1.2</container>
</requirements>
<entry_points>
<entry_point name="Climate Interactive Tool" requires_domain="True">
<port>8888</port>
<url>ipython/lab</url>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="API_KEY" inject="api_key" />
</environment_variables>
<command detect_errors="aggressive"><![CDATA[
#import re
export GALAXY_WORKING_DIR=`pwd` &&
mkdir -p ./jupyter/outputs/ &&
mkdir -p ./jupyter/data &&
#if $input:
#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($input.element_identifier))
get -t hid -i '${input.hid}' &&
ln -sf '/import/${input.hid}' './jupyter/data/${cleaned_name}' &&
#end if
## change into the directory where the notebooks are located
cd ./jupyter/ &&
export PATH=/home/jovyan/.local/bin:\$PATH &&
#if $mode.mode_select == 'scratch':
## copy default notebook
cp '$__tool_directory__/default_notebook.ipynb' ./ipython_galaxy_notebook.ipynb &&
jupyter trust ./ipython_galaxy_notebook.ipynb &&
jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
#else:
#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($mode.ipynb.element_identifier))
get -t hid -i '${mode.ipynb.hid}' &&
ln -sf '/import/${mode.ipynb.hid}' './data/${cleaned_name}' &&
jupyter trust ./data/${cleaned_name} &&
#if $mode.run_it:
jupyter nbconvert --to notebook --execute --output ./ipython_galaxy_notebook.ipynb --allow-errors ./*.ipynb &&
#else:
jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
#end if
cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
#end if
]]>
</command>
<inputs>
<conditional name="mode">
<param name="mode_select" type="select" label="Do you already have a notebook?" help="If not, no problem we will provide you with a default one.">
<option value="scratch">Start with a fresh notebook</option>
<option value="previous">Load a previous notebook</option>
</param>
<when value="scratch"/>
<when value="previous">
<param name="ipynb" type="data" format="ipynb" label="IPython Notebook"/>
<param name="run_it" type="boolean" truevalue="true" falsevalue="false" label="Execute notebook and return a new one."
help="This option is useful in workflows when you just want to execute a notebook and not dive into the webfrontend."/>
</when>
</conditional>
<param name="input" type="data" optional="true" label="Include data into the environment"/>
</inputs>
<outputs>
<data name="jupyter_notebook" format="ipynb" label="Executed Climate Notebook"></data>
</outputs>
<tests>
<test expect_num_outputs="1">
<param name="mode" value="previous" />
<param name="ipynb" value="test.ipynb" />
<param name="run_it" value="true" />
<output name="jupyter_notebook" file="test.ipynb" ftype="ipynb"/>
</test>
</tests>
<help>
The Climate Notebook is based on Jupyter an open-source web application that allows you to create and share documents that contain live code, equations,
visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization,
machine learning, and much more.
Galaxy offers you to use Jupyter Notebooks directly in Galaxy accessing and interacting with Galaxy datasets as you like. A very common use-case is to
do the heavy lifting and data reduction steps in Galaxy and the plotting and more `interactive` part on smaller datasets in Jupyter.
You can start with a new Jupyter notebook from scratch or load an already existing one, e.g. from your collegue and execute it on your dataset.
If you have a defined input dataset you can even execute a Jupyter notebook in a workflow, given that the notebook is writing the output back to the history.
You can import data into the notebook via a predefined `get()` function and write results back to Galaxy with a `put()` function.
The Climate version of the Jupyter Notebook offers a lot of preinstalled tools for climate science. The list of packages is based on what is available
on the [Pangeo](http://pangeo.io/) platform and [Pangeo stacks](https://pangeo-data.github.io/pangeo-stacks/).
- **Core scipy packages**: numpy, scipy, matplotlib, pandas, xarray, sparse and sympy
- **Data science**: scikit-image, scikit-learn, dask-ml, tensorflow, keras, pytorch-cpu, dask_labextension
- **Visualization**: holoviews, panel, geoviews, hvplot, geoviews, datashader, seaborn, altair, descartes, folium, vega,
vega_datasets, palettable, cmocean,plotly, psy-maps, psy-reg, psyplot, psyplot-gui, psy-maps, psy-reg,
geopy, branca
- **Geospatial**: iris, cartopy, basemap, basemap-data-hires, geopandas, rasterio, netcdf4, erddapy, pydap, h5py, h5netcdf, regionmask and rio-cogeo
- **Geoscience related**: climlab, metpy, satpy, gsw, eofs, esmpy, xesmf, windspharm, rasterstats, geojsoncontour
- **Climate related**: pyaerocom, cdo, cdsapi, cfgrib, cis. esmvaltool, nc-time-axis, nco
- **Intake related**: intake, intake-xarray, intake-esm, fsspec and intake-stac
- **zarr related**: zarr, numcodecs, python-blosc, lz4, gcsfs, s3fs, tiledb-py
- **jupyter related**: ipyleaflet, papermill, jupytext, ipydatawidgets, sidecar
- **xarray related**: xgcm, xrft, xhistogram, xlrd, xrviz, climpred, pytide, pyinterp
- **misc**: python-wget, prefect, requests, pillow, pip, nbgitpuller, pysplit, biopython, bioblend and galaxy-ie-helpers
</help>
</tool>
tools/interactive/interactivetool_geoexplorer.xml
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<tool id="interactive_tool_geoexplorer" tool_type="interactive" name="geoexplorer" version="0.1">
<description>An interactive spatial analysis platform using ggvis and Leaflet</description>
<requirements>
<container type="docker">ylebras/geoexplorer-docker</container>
</requirements>
<entry_points>
<entry_point name="geoexplorer visualisation" requires_domain="True">
<port>3838</port>
<url>/sample-apps/SIG/</url>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="API_KEY" inject="api_key" />
</environment_variables>
<command><![CDATA[
mkdir -p /srv/shiny-server/data/ &&
cp '$infile' /srv/shiny-server/data/inputdata.txt &&
mkdir -p /var/log/shiny-server &&
chown shiny.shiny /var/log/shiny-server &&
exec shiny-server >> /var/log/shiny-server.log 2>&1
]]>
</command>
<inputs>
<param name="infile" type="data" format="tabular,csv" label="csv file"/>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help>
<![CDATA[
`GeoExplorer <https://radiant-rstats.github.io/docs/>`_ is An interactive spatial analysis platform using ggvis and Leaflet.
Author: David Stephens
App: http://www.davesteps.com/geoExploreR/
.. class:: infomark
**Input data file MUST have as a uniq ID per row on first column, longitude column on column 2, latitude column on column 3 and quantitative values on 4th column **
Example input file (csv)::
"ID" "x" "y" "test"
01 -60.291838 46.328137 2
02 -114.58927 35.022485 3
03 -93.37406 30.00586 4
04 -79.336288 43.682218 5
05 -109.156024 31.904185 2
06 -71.098031 42.297408 9
07 -110.927215 32.18203 12
]]>
</help>
<citations>
<citation type="bibtex">@misc{githubsurvey2018,
author = {davesteps},
title = {{dashboard to visualise geographic data}},
publisher = {Github},
url = {https://github.com/davesteps/geoExploreR}
}
}</citation>
</citations>
</tool>
tools/interactive/interactivetool_guacamole_desktop.xml
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<tool id="interactive_tool_guacamole_desktop" tool_type="interactive" name="Ubuntu XFCE Desktop" version="0.1">
<requirements>
<container type="docker">quay.io/bgruening/guacamole-desktop</container>
</requirements>
<entry_points>
<entry_point name="Remote Desktop" requires_domain="True">
<port>8080</port>
<url><![CDATA[?username=user&password=password]]></url>
</entry_point>
</entry_points>
<command detect_errors="exit_code"><![CDATA[
sudo chmod 667 /tmp/ -R &&
sudo -E /startup.sh
]]>
</command>
<inputs>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help>
Simple Ubuntu XFCE all-in-one desktop. The Username is "user" and the Password is "password".
This image is based on the awesome work from CyVerse.
</help>
</tool>
tools/interactive/interactivetool_higlass.xml
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<tool id="interactive_tool_higlass" tool_type="interactive" name="HiGlass" version="1.8.0">
<description>an interactive Hi-C data visualizer</description>
<requirements>
<container type="docker">quay.io/bgruening/galaxy-higlass</container>
</requirements>
<entry_points>
<entry_point name="HiGlass Visualisation on $matrix.display_name" requires_domain="True">
<port>80</port>
</entry_point>
</entry_points>
<command><![CDATA[
cp '$matrix' /tmp/matrix.cool &&
cd /home/higlass/projects &&
mkdir -p data/log/ &&
supervisord &&
sleep 5 &&
python higlass-server/manage.py ingest_tileset --filename /tmp/matrix.cool --filetype cooler --datatype matrix &&
tail -f /var/log/supervisor/*
]]>
</command>
<inputs>
<param name="matrix" type="data" format="mcool" label="Cool file with multiple resolutions"/>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help>
Interactive tool for visualising Hi-C data, works only for multi-cooler files which store multiple resolutions.
For a detailed documentaition please visit https://docs.higlass.io/.
</help>
<xml name="citations">
<citations>
<citation type="doi">10.1186/s13059-018-1486-1</citation>
</citations>
</xml>
</tool>
tools/interactive/interactivetool_openrefine.xml
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<tool id="interactive_tool_openrefine" tool_type="interactive" name="OpenRefine" version="0.1">
<description>Working with messy data</description>
<requirements>
<container type="docker">ylebras/openrefine-docker</container>
</requirements>
<entry_points>
<entry_point name="Openrefine visualisation" requires_domain="True">
<port>3333</port>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="API_KEY" inject="api_key" />
</environment_variables>
<configfiles>
<configfile name="start_openrefine"><![CDATA[
exec /OpenRefine/refine -i 0.0.0.0 -m \$GALAXY_MEMORY_MB &
##Check if openrefine is up to work
STATUS=\$(curl --include 'http://127.0.0.1:3333' 2>&1)
while [[ \${STATUS} =~ "refused" ]]
do
echo "Waiting for openrefine: \$STATUS \n"
STATUS=\$(curl --include 'http://127.0.0.1:3333' 2>&1)
sleep 4
done
]]>
</configfile>
</configfiles>
<command detect_errors="aggressive"><![CDATA[
cp '$infile' /import/input.tabular &&
cat '$start_openrefine' &&
bash '$start_openrefine' &&
## Createnew project with the dataset
cd /refine-python &&
python openrefine_create_project_API.py '/import/input.tabular' &&
tail -f /dev/null
]]>
</command>
<inputs>
<param name="infile" type="data" format="tabular" label="tabular file"/>
</inputs>
<outputs>
<data name="outfile" format="tabular" />
</outputs>
<tests>
<test expect_num_outputs="1">
</test>
</tests>
<help><![CDATA[
`Openrefine <https://openrefine.org/>`_ (previously Google Refine) is a powerful tool for working with messy data:
cleaning it; transforming it from one format into another; and extending it with web services and external data.
.. class:: infomark
Example input file (TAB separated)::
"name" "longitude" "latitude" "countryCode"
Accipiter striatus Vieillot, 1808 -60.291838 46.328137 CA
Accipiter striatus Vieillot, 1808 -114.58927 35.022485 US
Accipiter striatus Vieillot, 1808 -93.37406 30.00586 US
Accipiter striatus Vieillot, 1808 -79.336288 43.682218 CA
Accipiter striatus Vieillot, 1808 -109.156024 31.904185 US
Accipiter striatus Vieillot, 1808 -71.098031 42.297408 US
Accipiter striatus Vieillot, 1808 -110.927215 32.18203 US
]]>
</help>
</tool>
tools/interactive/interactivetool_panoply.xml
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<tool id="interactive_tool_panoply" tool_type="interactive" name="Panoply" version="@VERSION@">
<description>interative plotting tool for geo-referenced data</description>
<macros>
<token name="@VERSION@">4.5.1</token>
</macros>
<requirements>
<container type="docker">quay.io/nordicesmhub/docker-panoply:@VERSION@</container>
</requirements>
<entry_points>
<entry_point name="Panoply on $infile.display_name" requires_domain="True">
<port>5800</port>
</entry_point>
</entry_points>
<command detect_errors="exit_code">
<![CDATA[
mkdir output &&
mkdir /config/home &&
mkdir /config/home/output &&
export HOME=/config/home &&
cp '$infile' '/config/home/$infile.display_name' &&
mkdir /config/home/.gissjava &&
cd /config/home/.gissjava &&
tar xvf /opt/PanoplyJ/colorbars.tar &&
cd - &&
/init ;
echo "Galaxy Panoply version @VERSION@" > output/version.txt &&
cp /config/home/output/* output/ | true &&
cd output &&
sleep 2 &&
for file in *; do mv "\$file" "\${file// /_}"; done &&
for file in *; do mv "\$file" "\$file.\${file\#\#*.}"; done
]]>
</command>
<inputs>
<param name="infile" type="data" format="netcdf,h5" label="netcdf"/>
</inputs>
<outputs>
<collection name="outputs" type="list" label="Panoply outputs">
<discover_datasets pattern="__name_and_ext__" directory="output" />
</collection>
</outputs>
<tests>
</tests>
<help><![CDATA[
`Panoply <https://www.giss.nasa.gov/tools/panoply/>`_ plots geo-referenced and other arrays from netCDF, HDF, GRIB, and other datasets.
]]>
</help>
</tool>
tools/interactive/interactivetool_paraview.xml
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<tool id="interactive_tool_paraview" tool_type="interactive" name="Paraview" version="0.1">
<requirements>
<!-- kitware/paraviewweb:pvw-v5.6.0-osmesa -->
<container type="docker">bmcv/galaxy-paraviewweb:latest</container>
</requirements>
<entry_points>
<entry_point name="Paraview based Visualisation of $infile.display_name" requires_domain="True">
<port>8777</port>
<!--url>apps/Visualizer</url-->
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="SERVER_NAME" strip="True">localhost:8080</environment_variable>
<environment_variable name="PROTOCOL">wss</environment_variable>
<environment_variable name="EXTRA_PVPYTHON_ARGS">-dr,--mesa-swr</environment_variable>
</environment_variables>
<command><![CDATA[
service nginx start &&
export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/lib/paraview-\$PV_VERSION_MAJOR/:/usr/local/lib/ &&
mkdir /input/ &&
#if $infile:
ln -s '$infile' /input/infile.${infile.ext} &&
#end if
Visualizer --paraview /usr/local/lib/paraview-\$PV_VERSION_MAJOR/
--data /input
--port 9777
--server-only
#if $infile:
--load-file infile.${infile.ext}
#end if
]]>
</command>
<inputs>
<param name="infile" type="data" format="png,jpg,tiff" optional="true" label="Optional input dataset"/>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help>
ParaView is an open-source, multi-platform application designed to visualize data sets of varying sizes from small to very large.
</help>
</tool>
tools/interactive/interactivetool_pyiron.xml
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<tool id="interactive_tool_pyiron" tool_type="interactive" name="PyIron Interactive Jupyter Notebook" version="0.1">
<requirements>
<container type="docker">quay.io/bgruening/docker-jupyter-notebook:pyiron</container>
</requirements>
<entry_points>
<entry_point name="PyIron Workbench" requires_domain="True">
<port>8888</port>
<url>ipython/lab</url>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="API_KEY" inject="api_key" />
<environment_variable name="PYIRONRESOURCEPATHS">/home/jovyan/resources</environment_variable>
</environment_variables>
<command detect_errors="aggressive"><![CDATA[
#import re
export GALAXY_WORKING_DIR=\${PWD} &&
export PYIRONPROJECTPATHS=\${PWD} &&
mkdir -p ./jupyter/outputs/ &&
mkdir -p ./jupyter/data &&
#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($input.element_identifier))
ln -sf '$input' './jupyter/data/${cleaned_name}' &&
## change into the directory where the notebooks are located
cd ./jupyter/ &&
cp \${HOME}/examples/* ./ &&
export PATH=/home/jovyan/.local/bin:\$PATH &&
#if $mode.mode_select == 'scratch':
## copy default notebook
cp '$__tool_directory__/default_notebook.ipynb' ./ipython_galaxy_notebook.ipynb &&
jupyter trust ./ipython_galaxy_notebook.ipynb &&
jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
#else:
#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($input.element_identifier))
cp '$mode.ipynb' ./${cleaned_name}.ipynb &&
jupyter trust ./${cleaned_name}.ipynb &&
#if $mode.run_it:
jupyter nbconvert --to notebook --execute --output ./ipython_galaxy_notebook.ipynb --allow-errors ./*.ipynb &&
#else:
jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
#end if
cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
#end if
]]>
</command>
<inputs>
<conditional name="mode">
<param name="mode_select" type="select" label="Do you already have a notebook?" help="If not, no problem we will provide you with a default one.">
<option value="scratch">Start with a fresh notebook</option>
<option value="previous">Load a previous notebook</option>
</param>
<when value="scratch"/>
<when value="previous">
<param name="ipynb" type="data" format="ipynb" label="IPython Notebook"/>
<param name="run_it" type="boolean" truevalue="true" falsevalue="false" label="Execute notebook and return a new one."
help="This option is useful in workflows when you just want to execute a notebook and not dive into the webfrontend."/>
</when>
</conditional>
<param name="input" type="data" optional="true" label="Include data into the environment"/>
</inputs>
<outputs>
<data name="jupyter_notebook" format="ipynb" label="PyIron Workbench"></data>
</outputs>
<tests>
<test expect_num_outputs="1">
<param name="mode" value="previous" />
<param name="ipynb" value="test.ipynb" />
<param name="run_it" value="true" />
<output name="jupyter_notebook" file="test.ipynb" ftype="ipynb"/>
</test>
</tests>
<help>
pyiron - an integrated development environment (IDE) for computational materials science. It combines several tools in a common platform:
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations,
visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization,
machine learning, and much more.
Galaxy offers you to use Jupyter Notebooks directly in Galaxy accessing and interacting with Galaxy datasets as you like. A very common use-case is to
do the heavy lifting and data reduction steps in Galaxy and the plotting and more `interactive` part on smaller datasets in Jupyter.
You can start with a new Jupyter notebook from scratch or load an already existing one, e.g. from your collegue and execute it on your dataset.
If you have a defined input dataset you can even execute a Jupyter notebook in a workflow, given that the notebook is writing the output back to the history.
You can import data into the notebook via a predefined `get()` function and write results back to Galaxy with a `put()` function.
</help>
</tool>
tools/interactive/interactivetool_radiant.xml
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<tool id="interactive_tool_radiant" tool_type="interactive" name="radiant" version="0.1">
<description>Data analytics using Radiant R Shiny app</description>
<requirements>
<container type="docker">ylebras/radiant-docker</container>
</requirements>
<entry_points>
<entry_point name="radiant visualisation" requires_domain="True">
<port>3838</port>
<url>/sample-apps/STAT/inst/app</url>
</entry_point>
</entry_points>
<environment_variables>
<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
<environment_variable name="API_KEY" inject="api_key" />
</environment_variables>
<command><![CDATA[
mkdir -p /srv/shiny-server/data/ &&
cp '$infile' /srv/shiny-server/data/inputdata.txt &&
mkdir -p /var/log/shiny-server &&
chown shiny.shiny /var/log/shiny-server &&
chown shiny.shiny /home/shiny/.Rprofile &&
exec shiny-server >> /var/log/shiny-server.log 2>&1
]]>
</command>
<inputs>
<param name="infile" type="data" format="tabular,csv" label="tabular or csv file"/>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help>
<![CDATA[
`Radiant <https://radiant-rstats.github.io/docs/>`_ is an open-source platform-independent browser-based interface for business analytics in R. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vincent Nijs. Please use the issue tracker on GitHub to suggest enhancements or report problems: https://github.com/radiant-rstats/radiant/issues. For other questions and comments please use radiant@rady.ucsd.edu.
Key features
- Explore: Quickly and easily summarize, visualize, and analyze your data
- Cross-platform: It runs in a browser on Windows, Mac, and Linux
- Reproducible: Recreate results and share work with others as a state file or an Rmarkdown report
- Programming: Integrate Radiant’s analysis functions with your own R-code
- Context: Data and examples focus on business applications
.. class:: infomark
Example input file (TAB separated)::
"name" "longitude" "latitude" "countryCode"
Accipiter striatus Vieillot, 1808 -60.291838 46.328137 CA
Accipiter striatus Vieillot, 1808 -114.58927 35.022485 US
Accipiter striatus Vieillot, 1808 -93.37406 30.00586 US
Accipiter striatus Vieillot, 1808 -79.336288 43.682218 CA
Accipiter striatus Vieillot, 1808 -109.156024 31.904185 US
Accipiter striatus Vieillot, 1808 -71.098031 42.297408 US
Accipiter striatus Vieillot, 1808 -110.927215 32.18203 US
]]>
</help>
<citations>
<citation type="bibtex">@misc{githubsurvey2018,
author = {vnijs},
title = {{Radiant - Business analytics using R and Shiny}},
publisher = {Github},
url = {https://github.com/vnijs/radiant}
}
}</citation>
</citations>
</tool>
tools/interactive/interactivetool_vcf_iobio.xml
+50
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<tool id="interactive_tool_vcf_iobio" tool_type="interactive" name="VCF (iobio) Visualisation" version="0.1">
<requirements>
<container type="docker">qiaoy/iobio-bundle.vcf-iobio:dev-ondemand</container>
</requirements>
<entry_points>
<entry_point name="VCF io.bio visualisation of $infile.display_name" requires_domain="True">
<port>80</port>
<url><![CDATA[/?vcf=http://localhost/tmp/vcffile.vcf.gz]]></url>
</entry_point>
</entry_points>
<command><![CDATA[
#set $PUB_HOSTNAME = 'localhost'
#set $PUB_HTTP_PORT = '80'
cd /var/www/html &&
sed -i "s@\"wss://services.iobio.io/vcfdepther/\"@((window.location.protocol === \"https:\") ? \"wss://\" : \"ws://\") + window.location.host + \"/vcfdepther/\"@" app/vcf.iobio.js &&
sed -i "s@\"wss://services.iobio.io/vcfstatsalive/\"@((window.location.protocol === \"https:\") ? \"wss://\" : \"ws://\") + window.location.host + \"/vcfstatsalive/\"@" app/vcf.iobio.js &&
sed -i "s@\"wss://services.iobio.io/tabix/\"@((window.location.protocol === \"https:\") ? \"wss://\" : \"ws://\") + window.location.host + \"/tabix/\"@" app/vcf.iobio.js &&
sed -i 's/deny all;//g' /etc/nginx/nginx.conf &&
ln -s '$infile' /input/vcffile.vcf.gz &&
#if $infile.metadata.tabix_index:
ln -s '${infile.metadata.tabix_index}' /input/vcffile.vcf.gz.tbi &&
#end if
head -n -2 /etc/supervisor.d/app.conf > /tmp/app.conf &&
mv /tmp/app.conf /etc/supervisor.d/app.conf &&
/usr/bin/supervisord -c /etc/supervisord.conf
]]>
</command>
<inputs>
<param name="infile" type="data" format="vcf_bgzip" label="Compressed VCF file"/>
</inputs>
<outputs>
<data name="outfile" format="txt" />
</outputs>
<tests>
</tests>
<help><![CDATA[
Examine your variant file in seconds with the VCF `iobio visualisation <https://vcf.iobio.io>`_.
This visualization is using Galaxy Interactive Tool and utilizes an all-in-one Docker container from http://iobio.io.
Make sure your VCF file is compressed to the vcf_bgzip datatype to load it into the Visualization.
]]>
</help>
</tool>