mirror of
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258 lines
15 KiB
XML
258 lines
15 KiB
XML
<tool id="interactive_tool_jupytool" tool_type="interactive" name="Interactive JupyTool and notebook" version="0.0.1" profile="22.01">
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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="staging_path_and_source_path"/>
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<configfile name="staging_script" filename="staging_script.py"><![CDATA[
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import json, os, shutil
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import os.path
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galaxy_inputs = "galaxy_inputs.json"
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galaxy_inputs_raw = "galaxy_inputs_raw.json"
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base_staging_path = "jupyter"
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base_data_staging_path = "galaxy_inputs"
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data_staging_path = os.path.join(base_staging_path, base_data_staging_path)
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os.makedirs(data_staging_path, exist_ok=True)
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shutil.copyfile(galaxy_inputs, os.path.join(data_staging_path, galaxy_inputs_raw))
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galaxy_input_dict = {}
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for user_input in json.load(open(galaxy_inputs, "r")).get("user_inputs", []):
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input_values = user_input['input_type']['input_value']
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input_name = user_input["input_name"]
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assert input_name not in galaxy_input_dict, ValueError("Each name can only be used once, but a duplicate was found: %s" % (input_name))
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if 'dataset' in user_input['input_type']['input_type_selector']:
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input_values2 = []
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if not isinstance(input_values, list):
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input_values = [input_values]
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for input_value_dict in input_values:
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path, filename = os.path.split(input_value_dict['staging_path'])
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input_value_dict2 = {"path": os.path.join(base_data_staging_path, input_name, path, filename), "metadata_files": []}
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## TODO: enable passing in all metadata values; ideally just handed off from original json creation
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path = os.path.join(data_staging_path, input_name, path)
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os.makedirs(path, exist_ok=True)
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try:
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os.symlink(input_value_dict['source_path'], os.path.join(path, filename))
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except FileExistsError:
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pass
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for metadata_value_dict in input_value_dict['metadata_files']:
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path, filename = os.path.split(metadata_value_dict['staging_path'])
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input_value_dict2["metadata_files"].append(os.path.join(base_data_staging_path, input_name, path, filename))
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path = os.path.join(data_staging_path, input_name, path)
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os.makedirs(path, exist_ok=True)
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try:
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os.symlink(metadata_value_dict['source_path'], os.path.join(path, filename))
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except FileExistsError:
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pass
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input_values2.append(input_value_dict2)
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input_values = input_values2
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galaxy_input_dict[input_name] = input_values
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with open(os.path.join(data_staging_path, galaxy_inputs), "w") as fh:
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json.dump(galaxy_input_dict, fh)
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]]>
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</configfile>
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<configfile name="galaxy_input_startup_script"><![CDATA[
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import json, os
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from galaxy_ie_helpers import get
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from galaxy_ie_helpers import put
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from galaxy_ie_helpers import get_galaxy_connection
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HISTORY_ID = os.environ.get('HISTORY_ID', None)
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try:
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GALAXY_INPUTS = json.load(open(os.path.join(os.environ.get("GALAXY_WORKING_DIR", ""), "jupyter", "galaxy_inputs", "galaxy_inputs.json")))
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except FileNotFoundError:
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GALAXY_INPUTS = {}
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]]>
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</configfile>
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</configfiles>
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<command detect_errors="aggressive"><![CDATA[
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python staging_script.py &&
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export GALAXY_WORKING_DIR=`pwd` &&
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mkdir -p ./jupyter/outputs/collection &&
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mkdir -p ./jupyter/galaxy_inputs &&
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## change into the directory where the notebooks are located
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cd ./jupyter/ &&
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export HOME=/home/jovyan/ &&
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export PATH=/home/jovyan/.local/bin:\$PATH &&
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cp '${galaxy_input_startup_script}' /home/jovyan/.ipython/profile_default/startup/00-load.py &&
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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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#import re
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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." 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="" label="Name for parameter" optional="False" help="Required. ASCII letters and numbers only.">
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<validator type="empty_field" message="Name is required"/>
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<sanitizer>
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<valid initial="string.ascii_letters,string.digits"/>
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</sanitizer>
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</param>
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<param name="description" type="text" label="Additional optional description" optional="true"/>
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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">Multiple datasets</option>
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<option value="dataset_collection">Dataset collection</option>
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<option value="dataset_collection_list">Dataset collection (list)</option>
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<option value="dataset_collection_paired">Dataset collection (paired)</option>
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<option value="dataset_collection_list_paired">Dataset collection (list:paired)</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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<option value="color">Color selector</option>
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<option value="dataset_optional" selected="true">Optional Dataset</option>
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<option value="dataset_multiple_optional">Optional Multiple datasets</option>
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<option value="dataset_collection_optional">Optional Dataset collection</option>
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<option value="dataset_collection_list_optional">Optional Dataset collection (list)</option>
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<option value="dataset_collection_paired_optional">Optional Dataset collection (paired)</option>
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<option value="dataset_collection_list_paired_optional">Optional Dataset collection (list:paired)</option>
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<option value="text_optional">Optional Text</option>
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<option value="integer_optional">Optional Integer</option>
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<option value="float_optional">Optional Floating point</option>
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<option value="boolean_optional">Optional Boolean</option>
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<option value="color_optional">Optional Color selector</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="false"/>
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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="false"/>
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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="false"/>
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</when>
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<when value="dataset_collection_list">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list" optional="false"/>
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</when>
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<when value="dataset_collection_paired">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="paired" optional="false"/>
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</when>
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<when value="dataset_collection_list_paired">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list:paired" optional="false"/>
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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="false"/>
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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="false"/>
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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="false"/>
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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" optional="false"/>
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</when>
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<when value="color">
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<param name="input_value" type="color" label="Select value" optional="false"/>
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</when>
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<when value="dataset_optional">
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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_optional">
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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_optional">
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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="dataset_collection_list_optional">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list" optional="true"/>
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</when>
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<when value="dataset_collection_paired_optional">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="paired" optional="true"/>
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</when>
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<when value="dataset_collection_list_paired_optional">
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<param name="input_value" type="data_collection" format="data" label="Select value" collection_type="list:paired" optional="true"/>
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</when>
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<when value="text_optional">
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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_optional">
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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_optional">
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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_optional">
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<param name="input_value" type="boolean" truevalue="true" falsevalue="false" label="Select value" optional="true"/>
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</when>
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<when value="color_optional">
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<param name="input_value" type="color" label="Select value" optional="true"/>
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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"/>
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<data name="output_dataset" format="data">
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<discover_datasets pattern="__designation_and_ext__" directory="jupyter/outputs" visible="true" assign_primary_output="true"/>
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</data>
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<collection name="output_collection" type="list" label="JupyTool output collection">
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<discover_datasets pattern="__designation_and_ext__" directory="jupyter/outputs/collection" visible="false"/>
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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 colleague 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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