A Jupyter-based UI for the Multiply project based on Jupyter Widgets and Bokeh.
The Multiply images include Jupyter Lab, a dedicated Multiply REST server and a Multiply Notebook API.
When the Docker is started on its host VM, the Jupyter Lab server and the Multiply REST server are started. User get a dedicated URL that brings up the Jupyter Lab in their browser. From Jupyter Lab they have access to
- Jupyter Notebooks that can import the Multiply Notebook API
- Terminal windows that allow accessing the container environment and mounted file systems.
The Multiply Notebook API provides a simple set of functions (API) that bring up dedicated UI forms, such as data query and job execution.
The API provides access to objects that are a result of the GUI interaction.
Users can further interact with such objects e.g. job = Job(13); job.cancel().
These objects also have dedicated HTML representations in the notebook. For example
a query result may render a table of data files, or a processing result
may render quicklooks.
Another set of functions may be provided for simple analysis and visualisation of the processing results.
For new users, we will provide a set of Notebooks for the most common Multiply use cases. Users can use them as starting points.
- With Jupyter Lab, users can
- create and modify any number of wokflows and data anlyses and store them in their workspaces;
- have numerous notebooks and output displays side by side;
- use a variability of data visualisations already available.
- Very flexible, users can bring up UIs anywhere in the flow and interact with the objects they produce such as jobs, queries.
- Python programmers can easily extend the UI capabilities by writing new UI, analysis and visualisation functions.
- Custom widgets can be implemented using JavaScript. This allows for integration of popular and powerful JS visualisation libraries (e.g. Leaflet Map, D3)
- In the Notebooks, users can exploit the power of numerous popular Python data science packages (xarray, numpy, scipy, pandas, ...)
Create environment:
$ cd multiply-ui
$ conda env create
Activate environment and install sources:
$ conda activate multiply-ui
You will also need to install the MULTIPLY Core and Data Access components components. After you have checked out the source code from github, you can install the packages with
$ python setup.py develop
Install jupyter-widgets and jupyter-leaflet extension for Jupyter-Lab
$ jupyter labextension install @jupyter-widgets/[email protected]
$ jupyter labextension install [email protected]
Install the MULTIPLY extension for Jupyter-Lab
$ cd js
$ npm install
$ cd ..
4 jupyter labextension install js
Install multiply-ui from source code:
$ python setup.py develop
Run multiply-ui web service:
$ mui-server
Run Jupyter Lab
$ jupyter-lab notebooks/mui-demo.ipynb
Note for developers: For automatically building the JavaScript code every time there is a change, run the following command from the /js/ directory:
$ npm run watch
And to run Jupyter Lab, use this command:
$ jupyter lab --watch notebooks/mui-demo.ipynb
Every time a JavaScript build has terminated you need to refresh the Notebook page in order to load the JavaScript code again.
Note for developers: For automatically building the JavaScript code every time there is a change, run the following command from the /js/ directory:
$ npm run watch
And in a separate terminal:
$ jupyter lab --watch
Every time a JavaScript build has terminated you need to refresh the Notebook page in order to load the JavaScript code again.
-
Jupyter Lab: https://jupyterlab.readthedocs.io
-
Jupyter Widgets: https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html
-
Bokeh docs: https://bokeh.pydata.org/en/latest/docs/user_guide/notebook.html
-
Bokeh examples: https://github.com/bokeh/bokeh/tree/1.0.4/examples/howto/notebook_comms
-
Integrating your objects with IPython: https://ipython.readthedocs.io/en/stable/config/integrating.html