OneMap OneWheel Ride Aggregator

This project is designed to aggregate and visualize a user's OneWheel app rides based on a user-specified location.

NOTE: Recorded ride must be set to "public" within the OneWheel app in order to be captured and included in the aggregation.

How?

1. Fetching General Ride Data:

The program starts by retrieving general ride details from the endpoint:

https://app.onewheel.com/wp-json/fm/v2/trails

2. User Input:

Initial Run :

• If ONEMAP_LOCATION, ONEMAP_MAX_DISTANCE, or ONEMAP_NICKNAME are not set in config.json, the user is prompted to provide:

  • A location (e.g., 'Buffalo, NY')

  • Maximum search distance in miles

  • OneWheel app nickname (as displayed on leaderboards)

    

• These inputs are then saved to config.json for future runs.

Subsequent Runs :

• The application uses the values stored in config.json without prompting the user.

3. Filtering Rides:

With the user input, the program filters rides based on the provided location using the haversine_distance function. This function calculates the distance between two latitude and longitude points and helps identify rides within the specified radius.

4. Augmenting Ride Data:

Before diving into the augmentation process, it's important to note that the trail and localized filter data fetched initially will include ALL rides in the specified area. The program then individually checks each ride to determine if the user matches the provided input. Due to this, if a large area is specified, the process can be time-consuming.

For each filtered ride with an ID greater than the highest existing ID in user_rides.json:

• Details are fetched from:

https://app.onewheel.com/wp-json/fm/v2/trails/1?trackId={ride_id}

• If the ride matches the user's nickname, further detailed coordinates for the ride are fetched from:

https://app.onewheel.com/wp-json/fm/v2/trailscoordinates/{ride_id}

This data is saved locally for further processing.

5. Visualization:

After aggregating ride data, the folium library is utilized to plot the rides on a dark-themed map. Each ride is represented as a path.

Example Map

To view and explore the aggregated map:

1. Navigate to the local path where rides_map_dark.html is saved.
2. Open rides_map_dark.html in your preferred web browser.

Prerequisites

• Python 3.x
• Required Python packages: requests, folium, geopy

Setup

1. Clone the repository:

   git clone https://github.com/whoalacoasta/onemap

2. Set up a virtual environment:

   Navigate to the project directory and create a virtual environment:

   python -m venv .venv

   Activate the virtual environment:

   • On Windows:

     .\.venv\Scripts\activate

   • On macOS and Linux:

     source .venv/bin/activate

3. Install required packages:

   pip install -r requirements.txt

Configuration:

1. Rename config_example.py to config.py:

   mv config_example.py config.py

2. Update config.py with the necessary headers and cookies:

   These can be obtained by inspecting the network requests made while browsing public rides.

   • Open the OneWheel browser app.
   • Right-click and select 'Inspect' or press Ctrl+Shift+I (or Cmd+Option+I on Mac).
   • Navigate to the 'Network' tab.
   • Refresh the page or interact with the website or perform actions that trigger the relevant requests (e.g., viewing ride details).
   • Filter the requests to locate the specific endpoints used in this project, such as https://app.onewheel.com/wp-json/fm/v2/trails and related endpoints (e.g., filtering by "v2" would work).
   • Once you identify a relevant request, click on it and copy the 'Request Headers' and 'Cookies' to use in the config.py file.

3. Initial User Input:

   On the first run, if ONEMAP_LOCATION, ONEMAP_MAX_DISTANCE, or ONEMAP_NICKNAME are not set in config.json, the program will prompt for these inputs:

   • Location: E.g., 'Buffalo, NY'
   • Maximum Distance: In miles
   • Nickname: Your OneWheel app nickname

   These inputs will be saved to config.json for future runs, eliminating the need for repeated input. Subsequent runs will use the saved values.

Usage

Navigate to the project directory (ensure your virtual environment is activated) and execute:

python run.py

Dashboard:

Disclaimer: The api does specify the units for some fields. As such, units and subsequent unit conversions used in the dashboard are probably not accurate - contributions are welcome.

After you've aggregated your rides using the main script (and it created a data/user_rides.json), you can visualize some of your ride stats using the plot_stats.py script.

Steps to Generate the Dashboard:

1. Ensure you've already executed run.py and have user_rides.json generated.

2. Navigate to the project directory (ensure your virtual environment is activated) and execute:

   python viz/plot_stats.py

3. This will do two things:

   (a) Generate a dashboard.png file.

   (b) Open an interactive window containing the dashboard (If a GUI is available).

Example dashboard:

Contributing

Pull requests are welcome. For significant changes, please open an issue first to discuss.

License

MIT