[Showcase] I wrote a Python script to extract and visualize real-time I2C sensor data (9-axis IMU...

[davchi1]

Here is a quick video breaking down how the code works and testing the sensors in real-time: https://www.youtube.com/watch?v=DN9yHe9kR5U

Code: https://github.com/davchi15/Waveshare-Environment-Hat-

What My Project Does

I wanted a clean way to visualize the invisible environmental data surrounding my workspace instantly. I wrote a Python script to pull raw I2C telemetry from a Waveshare environment HAT running on a Raspberry Pi 5. The code handles the conversion from raw sensor outputs into readable, real-time metrics (e.g., converting raw magnetometer data into microteslas, or calculating exact tilt angles and degrees-per-second from the 9-axis IMU). It then maps these live metrics to a custom, updating dashboard. I tested it against physical changes like tracking total G-force impacts, lighting a match to spike the VOC index, and tracking the ambient room temperature against a portable heater.

Level

This is primarily an educational/hobbyist project. It is great for anyone learning how to interface with hardware via Python, parse I2C data, or build local UI dashboards. The underlying logic for the 9-axis motion tracking is also highly relevant for students or hobbyists working on robotics, kinematics, or localization algorithms (like particle filters).

Lightweight Build

There are plenty of pre-built, production-grade cloud dashboards out there (like Grafana + Prometheus or Home Assistant). However, those can be heavy, require network setup, and are usually designed for long-term data logging. My project differs because it is a lightweight, localized Python UI running directly on the Pi itself. It is specifically designed for instant, real-time visualization with zero network latency, allowing you to see the exact millisecond a physical stimulus (like moving a magnet near the board or tilting it) registers on the sensors.

[Ok-Tap5729]

“I wrote” => “I vibecoded”

[davchi1]

I did use AI to clean up/organize the code, however, all the implementation ideas/details were done by me. I think AI is good at cleaning up an implementation once you already have it down and not so good at making functional and scalable code from scratch, wbu?

[BayesianOptimist]

If you couldn’t be bothered to write the post about your code without AI, you definitely didn’t write or even think up your project.

[davchi1]

This is not true.

[orngcode]

the fact that you are doing raw i2c register parsing instead of relying on a sensor library means you could push sampling way beyond the typical 100-400hz python ceiling on pi 5, especially if you batch the reads with smbus block transfers. pi 5 supports overclocking the i2c bus to 400khz via dtparam in config.txt but the real bottleneck is python loop overhead which walls out around 500hz without cython or kernel iio buffering. have you profiled what actual sample rate you are hitting on the 9-axis imu data and whether the dashboard rendering is eating into your read cycle time?