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Add JupyTool interactive tool.
This commit is contained in:
Daniel Blankenberg
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<tool id="interactive_tool_jupytool" tool_type="interactive" name="Interactive JupyTool and notebook" version="0.0.1">
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<requirements>
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<container type="docker">quay.io/bgruening/docker-jupyter-notebook:ie2</container>
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</requirements>
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<entry_points>
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<entry_point name="JupyTool interactive tool" requires_domain="True">
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<port>8888</port>
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<url>ipython/lab</url>
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</entry_point>
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</entry_points>
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<environment_variables>
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<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
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<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
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<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
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<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
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<environment_variable name="API_KEY" inject="api_key" />
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</environment_variables>
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<configfiles>
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<inputs name="inputs" filename="galaxy_inputs.json" /> <!-- data_style="paths" /> data_collection doesnt support paths-->
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</configfiles>
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<command detect_errors="aggressive"><![CDATA[
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#import re
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export GALAXY_WORKING_DIR=`pwd` &&
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mkdir -p ./jupyter/outputs/ &&
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mkdir -p ./jupyter/data &&
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mkdir -p ./jupyter/inputs &&
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cp 'galaxy_inputs.json' './jupyter/inputs/galaxy_inputs.json' &&
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##set $cleaned_name = re.sub('[^\w\-\.]', '_', str($input.element_identifier))
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##ln -sf '$input' './jupyter/data/${cleaned_name}' &&
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#for $user_input in $user_inputs:
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#if str($user_input.input_type.input_type_selector) == "dataset":
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ln -sf '${user_input.input_type.input_value}' './jupyter/data/${user_input.input_name}' &&
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#elif str($user_input.input_type.input_type_selector) == "dataset_multiple":
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#for $i, $inp in enumerate($user_input.input_type.input_value):
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ln -sf '${inp}' './jupyter/data/${i}_${user_input.input_name}' &&
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#end for
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#elif str($user_input.input_type.input_type_selector) == "dataset_collection":
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mkdir -p ./jupyter/data/${user_input.input_name} &&
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#for $inp in $user_input.input_type.input_value:
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#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($inp.element_identifier))
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ln -sf '${inp}' './jupyter/data/${user_input.input_name}/${cleaned_name}' &&
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#end for
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#end if
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#end for
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## change into the directory where the notebooks are located
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cd ./jupyter/ &&
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export PATH=/home/jovyan/.local/bin:\$PATH &&
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#if $mode.mode_select == 'scratch':
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## copy default notebook
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cp '$__tool_directory__/default_notebook.ipynb' ./ipython_galaxy_notebook.ipynb &&
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jupyter trust ./ipython_galaxy_notebook.ipynb &&
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jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
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cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
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#else:
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#set $cleaned_name = re.sub('[^\w\-\.]', '_', str($mode.ipynb.element_identifier))
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cp '$mode.ipynb' ./${cleaned_name}.ipynb &&
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jupyter trust ./${cleaned_name}.ipynb &&
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#if $mode.run_it:
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jupyter nbconvert --to notebook --execute --output ./ipython_galaxy_notebook.ipynb --allow-errors ./*.ipynb &&
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#else:
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jupyter lab --allow-root --no-browser --NotebookApp.shutdown_button=True &&
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#end if
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cp ./ipython_galaxy_notebook.ipynb '$jupyter_notebook'
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#end if
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]]>
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</command>
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<inputs>
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<conditional name="mode">
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<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.">
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<option value="scratch">Start with a fresh notebook</option>
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<option value="previous">Load a previous notebook</option>
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</param>
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<when value="scratch"/>
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<when value="previous">
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<param name="ipynb" type="data" format="ipynb" label="IPython Notebook"/>
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<param name="run_it" type="boolean" truevalue="true" falsevalue="false" label="Execute notebook and return a new one."
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help="This option is useful in workflows when you just want to execute a notebook and not dive into the webfrontend."/>
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</when>
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</conditional>
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<repeat name="user_inputs" title="User inputs">
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<param name="input_name" type="text" value="" optional="False"/>
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<conditional name="input_type">
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<param name="input_type_selector" type="select" label="Choose the input type">
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<option value="dataset" selected="true">Dataset</option>
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<option value="dataset_multiple">Dataset Multiples</option>
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<option value="dataset_collection">Dataset Collection</option>
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<option value="text">Text</option>
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<option value="integer">Integer</option>
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<option value="float">Floating Point</option>
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<option value="boolean">Boolean</option>
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</param>
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<when value="dataset">
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<param name="input_value" type="data" format="data" label="Select value" multiple="false" optional="true"/>
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</when>
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<when value="dataset_multiple">
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<param name="input_value" type="data" format="data" label="Select value" multiple="true" optional="true"/>
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</when>
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<when value="dataset_collection">
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<param name="input_value" type="data_collection" format="data" label="Select value" optional="true"/>
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</when>
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<when value="text">
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<param name="input_value" type="text" value="" label="Select value" optional="true"/>
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</when>
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<when value="integer">
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<param name="input_value" type="integer" value="" label="Select value" optional="true"/>
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</when>
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<when value="float">
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<param name="input_value" type="float" value="" label="Select value" optional="true"/>
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</when>
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<when value="boolean">
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<param name="input_value" type="boolean" truevalue="true" falsevalue="false" label="Select value" />
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</when>
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</conditional>
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</repeat>
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</inputs>
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<outputs>
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<data name="jupyter_notebook" format="ipynb" label="Executed JupyTool Notebook"></data>
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<data name="output_dataset" format="data" label="JupyTool dataset"></data>
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<collection name="output_collection" type="list" label="JupyTool collection" hidden="False">
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<discover_datasets pattern="__designation_and_ext__" directory="jupyter/outputs/" visible="true"/>
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</collection>
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</outputs>
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<tests>
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<test expect_num_outputs="1">
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<param name="mode" value="previous" />
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<param name="ipynb" value="test.ipynb" />
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<param name="run_it" value="true" />
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<output name="jupyter_notebook" file="test.ipynb" ftype="ipynb"/>
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</test>
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</tests>
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<help>
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Welcome to the JupyTool! Here you can create, run, and share custom Galaxy tools based upon Jupyter Notebooks.
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The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations,
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visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization,
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machine learning, and much more.
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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
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do the heavy lifting and data reduction steps in Galaxy and the plotting and more `interactive` part on smaller datasets in Jupyter.
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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.
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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.
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You can import data into the notebook via a predefined `get()` function and write results back to Galaxy with a `put()` function.
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</help>
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</tool>
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