frc-maps

Purpose

The purpose of this project is to generate geographic data visualizations for the FIRST Robotics Competition.

Scripts

note: you should be in the usa-counties directory (or sub-directory) to run these scripts

• generate_random_colors.py: a test script that interfaces with the County SVG code to ensure we can access each county, change its color + title
• generate_team_density_map.py: generates a county map with colors corresponding to how many teams are in that county and titles for each county with team keys (prereq: output of load_team_locations.py). Also generates a markdown table.
• load_team_locations.py: generate a JSON file with a mapping from team key -> [county codes]
• generate_event_density_map.py: generates a county map with colors corresponding to how many events are in that county and titles for each county with event keys (prereq: output of load_event_locations.py). Also generates a markdown table.
• load_event_locations.py: generate a JSON file with a mapping from event key -> [county codes]
• fetch_frc_colors.py: calls the TBA API to get all teams for a year, and then calls the frc-colors API to get primary colors for each team to generate a JSON file with a mapping from team key -> color
• generate_starting_ownership_file.py: generates a JSON file with a mapping from county code -> [team keys] with the initial ownership of each county (based on closest teams owning each county)
• generate_map_from_starting_ownership_file.py: generates a county map with colors corresponding to the ownership of each county and titles for each county with team keys (prereq: output of generate_starting_ownership_file.py).
• generate_undefeated_ownership_file.py: generates a JSON file that consumes the starting ownership file and pulls match data for weeks of the season to generate a new ownership file based on who has won/lost their matches
• generate_undefeated_ownership_maps.py: generates a series of maps based on the undefeated ownership files

Setup

1. Create a .env in the usa-counties/src folder that matches the .env.example template located in the same folder.
2. As of now, you must be in the usa-counties/src directory to run commands (ex: python3 ./generate_random_colors.py)

• Eventually I want to make this runnable from the main directory

3. Create a conda environment with conda create --name frc-maps --file environment.yml
   • use conda env update --file environment.yml --prune to update your environment based on the file
   • update this file manually and then use this command to sync your environment
4. Activate the conda environment with conda activate frc-maps

Data Sources

• In order to determine what county each team is in, we are using the following US City dataset: https://simplemaps.com/data/us-cities
• If the city dataset does not work, the backup is this zip code dataset: https://www.kaggle.com/datasets/danofer/zipcodes-county-fips-crosswalk