Learn how to work with Lexcube, a Python package for data visualization in the space-time domain!
- 🌟 Introduction
- 🌐 Lexcube
- 📰 Data
- 📂 Data Cube with Random Numbers
- 🗂️ Data Cube with Climate Data
- 🔄 Raster Layers to Xarray
- 🌍 3D Visualization of Xarray by Lexcube
- 📦 What Else Can We Do with Lexcube
- 📝 Conclusion
- 📚 References
🌟 Introduction
Data visualization in three dimensions (latitude, longitude, and time) is fascinating, isn’t it? As a geospatial data scientist, I have always wanted to know the easiest way to plot a cubic dataset created by merging hundreds of raster layers. While reading my feeds on LinkedIn, I found a great Python library called Lexcube, which has recently become available for Jupyter Notebook. For additional information about Lexcube, please refer to this article and/or check out Lexcube on GitHub.
First of all, I’d like to thank Miguel Mahecha for sharing that post on LinkedIn and also Maximilian Söchting and his team for developing a valuable tool for the geospatial data community. Secondly, here is a hands-on exercise to help you use this package to visualize your cubic data in a 3D plot. All the steps have been coded in Python in Google Colab and by the end of this story, you will learn how to convert your raster layers to the Xarray format and then use it in Lexcube to create a 3D plot of your data.
If, like me, you were searching for a package for 3D visualization of your data, this story is for you. I have no affiliation with Lexcube and just wanted to my experience by writing this blog post.
🌐 Lexcube
Leipzig Explorer of Earth Data Cubes, or Lexcube, is an interactive data visualization tool developed by Maximilian Söchting as a Ph.D. project under the supervision of Gerik Scheuermann and Miguel Mahecha at Leipzig University. The tool is designed to handle large Earth data cubes. The project received funding from several institutions and agencies, including the European Space Agency (ESA). In May 2022, a web version of this tool became…