Bay Area Skydiving 2018 Tracking Competition

This project contains the data files for scoring the 2018 Tracking Competition, an annual event hosted by Bay Area Skydiving in Byron, CA.

The setup includes a Jupyter Lab dockerized implementation with local data storage and single user access credentials.

The data are organized in the scoring directory in the layout prescribed by the FlySafe BASE distance analysis tool.

Requirements

Quick start

FlySightViewer.app for viewing the FlySight CSV files
Microsoft Excel to view the scoring tables
FlySafe for distance analysis

Developers

Latest Docker for Linux, MacOS, or Windows
Firefox, Chrome, Safari, or any modern browser
Basic knowledge of Jupyter Lab

Jupyter Lab

First time launch

Docker will pull the latest version of Jupyter Lab and run it. This process may take between 30 seconds and a few minutes, depending on the speed of the user's Internet connection.

First time run generates a token to set the notebook password. This password is necessary to open the run-time environment because this notebook can run in a server open to the whole Internet, if desired. The password set up is a one-time event.

To generate the first time password, open a terminal shell in the basdtracks2018 directory, then execute:

docker-compose up --remove-orphans ; docker-compose rm

This is a good time for the user to get some coffee ☕️ if they have a slow Internet connection.

Jupyter Lab starts when the download is complete, and shows the legend:

notebook    |     Copy/paste this URL into your browser when you connect for the first time,
notebook    |     to login with a token:
notebook    |         http://b8b27455b503:8888/?token=6c7546c58eff46029e095ea0f8e95f5006fd66dfd9fa63d6&token=6c7546c58eff46029e095ea0f8e95f5006fd66dfd9fa63d6

Generate a new password by:

Log on to http://localhost:8888 on your web browser
Enter the token in the Setup a Password section, and enter a password of choice. The token, in our example, is the string of numbers and letters after token=, or 6c7546c58eff46029e095ea0f8e95f5006fd66dfd9fa63d6 in the example
Click on Login and set new password to go to the work bench
Click on the Logout button at the top right corner
Go to the terminal and stop the Jupyter container by pressing Ctrl-C
Respond "y" to the Going to remove notebook prompt

Done! You may validate that the first time run was successful by running, this command:

if [[ -e "scoring/_jupyter/jupyter_notebook_config.json" ]]; then echo 'Success!'; else echo "Failed - try again"; fi

Work with the data

The process is super simple now:

Start the Jupyter Lab container
Log on to Jupyter at http://localhost:8888
Play with the data to your heart's content!

To start:

docker-compose up --remove-orphans ; docker-compose rm

When done, kill the process with Ctrl-C or turn off the computer. That's it!

Working with data

**Section under construction ***

FAQ

How did you calculate the tracking distance? - By taking the first point during jump run before the first constant descent, down to the 3,000 ft AGL point or the parachute deployment point, and using the difference in the distance traveled that FlySight reported.
How accurate were the measurements? - The FlySightViewer zoom range changed between measurements when the map was on display, so two consecutive reads, one with and one without the map in the window, for the same competitor, showed a difference of 20-100 ft in either direction. Whenever possible the window size remained the same and the map was hidden.
How were the FlySights configured? - They came configured from Bionic Avionics and we used them as-is. While the competitors were welcome to use their personal FlySights along with the competition devices, the enclosed data set only includes the competition configurations.
Do you support Windows? - Sort of. All the tools used for analyzing the competition data are supposed to work under Windows, but the developers don't have Windows systems to test. Fork the project or ask to join if you'd like to help!

pr3d4t0r / basdtracks2018