At Trendence, we are using Cube.js to serve some of our data. We want to visualize the resulting data with the manifold power of Highcharts in our apps, but also within Jupyter notebook environments.
To achieve this, we have written an API connecting both parts. Our API can read a Trendence-specific DSL for visualizations that we call TREVL ("Trendence Visualization Language") where we can reference data cubes and define visualization options.
PyTrevl is a Python package that makes generating Highcharts viz from cube data easy and helps our Data Scientists with the generation of TREVL code, enabling them to experiment with our data served by Cube in a Jupyter notebook environment.
We are strong believers in the power of Open Source Software. TREVL itself works currently only with a proprietary API that we are not entirely ready to share at this stage, but PyTrevl also offers the opportunity to plug in any Cube connection and we believe other people may are also interested in combination of Cube.js and Highcharts within Jupyter notebooks environments.
So our goal is to provide a little foundational step here. And because we are excited about it, you are maybe, too 🤓 If you are interested, feel free to play around with PyTrevl. In case of questions or Feedback, open a GitHub Issue or reach out to us via pytrevl@trendence.com
You just have to set some ENV variables. Details coming Soon 🤗 🚧
You can use a locally running, containerized Jupyter Lab using your local
PyTrevl code like so:
-
(Only once) Create
.envenvironment file withX_MIDDLE_BASEURL=... X_MIDDLE_PASSWORD=... -
Start the notebook:
docker-compose --file contrib/docker-compose.yml up -d.NOTE: You can use
PYTREVL_LAB_PORT=3999 docker-compose ...to change the port. -
Open http://localhost:8888 in a browser. (Use a different port of you changed it.)
You can install the package frlocally by cd'ing into the repository directory and running
pip install -e .