Add ml notebook

tools/interactive/interactivetool_ml_jupyter_notebook.xml

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+<tool id="interactive_tool_ml_jupyter_notebook" tool_type="interactive" name="Interactive Machine Learning Jupyter Notebook" version="0.1">
+    <requirements>
+        <container type="docker">anupkumar/sample-docker-ml-jupyter:latest</container>
+    </requirements>
+    <entry_points>
+        <entry_point name="Jupyter 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 &&
+
+        #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/ &&
+        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="Executed 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 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>