Work

CODE@TACC 2022

Dev
Summer Camp
Python
UT Austin

Learning collected live data from Brakenridge Labratory and implementing that data into datasets and arrays to create a data model to stimulate lights showing the different temperatures, conditions, and the times of day.

A bright pink sheet of paper used to wrap flowers curves in front of rich blue background

CODE@TACC 2022 Summer Camp

Key Features

  • Dynamic Data Interpretation: Utilizes Pandas and Numpy for efficient data handling and analysis, processing temperature data from multiple CSV files.
  • Hardware Integration: Seamlessly integrates with Raspberry Pi’s GPIO interface, controlling LED outputs to reflect environmental data variations.
  • Adaptive Visualization: Employs an algorithm to interpolate sensor readings, correlating them with specific LED patterns for an intuitive understanding of temperature changes.
  • Multi-Site Capability: Versatile in handling data from various environmental sites, showcasing the program’s adaptability to different data sets.
  • User-Friendly Interaction: Ensures easy-to-understand visual feedback, making it suitable for educational purposes or for users with non-technical backgrounds.

Technical Stack

Python, RPi.GPIO, Pandas, Numpy, Matplotlib.

Application

Ideal for educational environments as a tool to demonstrate data visualization and hardware interaction, and can be adapted for practical environmental monitoring solutions.

Project Repository

CODE@TACC 2022 Final Project repo

Camp Information

CODE@TACC Connected repo

(c) 2022 Abdon Morales