Intro

Welcome to the folder containing all the BENNESAW BATE Racing data acquisition services. The entire system is Python and MatLab based.

This guide is split into two parts: a User's Guide and a Developer's Guide:

• If you will be working on these files (i.e. you are a data aq member), you will need to refer to the Developer's Guide
• Otherwise, the User's Guide should suffice for everyone else

If you have any questions or need troubleshooting, feel free to reach out to Mathewos Samson on Teams, Discord or via email: [email protected]

Setup

1. Python 3 is required, and may be installed here: https://www.python.org/downloads/. Make sure it is added to PATH
2. For MatLab (OPTIONAL), you will need an education/work license to get it for free. Follow GT's instructions here: https://www.mathworks.com/academia/tah-portal/kennesaw-state-university-31081932.html
   • Really the only thing you need MatLab for is to plot the data after parsing. Otherwise, just having Python is enough
3. If you have not done so already, clone this GitHub repo or download it as a zip, extract, and save to somewhere safe
4. Change directory to the repo. All you have to do is cd KS5e-Data-Logging
5. optional step, create a python virtual environment by running these commands: pip install virtualenv virtualenv venv .\venv\scripts\activate (https://stackoverflow.com/questions/41972261/what-is-a-virtualenv-and-why-should-i-use-one)
6. Once you are here, download the needed pip libraries by issuing the command pip install -r requirements.txt

User's Guide

If you are a user, everything you need to care about is in the telemetry_exe folder. Navigate to that directory.

There are two services: the Live Console and the Parser and Plotter

Live Console

1. Either run the file console_exe.py with the Python Interpreter or issue the command py -3 console_exe.py
2. right now this doesnt really work, you can run it with a test csv though

Parser and Plotter

1. Get the raw data CSVs from the SD card on the vehicle
2. Place them in the Raw_Data folder in this directory
3. Either run the file parser_exe.py with the Python Interpreter or issue the command py -3 parser_exe.py
4. Wait for the process to finish (a success message from parser_exe.py followed by termination)
5. You may now retrieve the parsed data from the Parsed_Data as well as the Better_Parsed_Data folder and the .mat file output.mat
   1. logs in Parsed_Data will be formatted a lil different than in Better_Parsed_Data, so peep both, but its the same data trust me

There is no need to delete the CSVs or the .mat file between use. However, the next time you run, be sure to delete the old CSVs from Raw_Data if desired. Otherwise, they will be parsed again.

The next steps are optional - only if you want to plot the result

6. Open dataPlots.m in MatLab
7. In MatLab, first load output.mat by double clicking it on the sidebar. Then click run on dataPlots.m
   • This script will not execute fully if there is not enough data, and it will stop on the first plot it is missing data for

Developer's Guide

Way too lazy to do right now, I'll get to it after comp hopefully