MathBunny / strava-wind-analysis

Analytics and data visualization site for Strava

Bringing machine learning analytics, data visualization and weather data to cycling activities and segments.

Features

• Strava OAuth authentication
• Performance analytics on historical segment and aggregate data
• Interactive charts using chart.js, Google Maps integration
• Wind analysis using DarkSky API and vector manipulation algorithm
• Individual modals with athlete-specific statistics on segment leaderboard
• Linear regression and interpolation tools using scikit-learn
• Segment and ride filtering options (distance, speed, etc.)
• Content-based segment recommendation engine for scrolling feed
• Improved performance with Redis caching
• Integration with MongoDB including weather API throttling and user profiles
• Legacy website support

Screenshots

Build, Configure and Run

First, install the dependencies. Ensure you have Node.js installed and npm:

npm install

Now setup the configuration file in the root folder as follows (saved as config.js):

const clientID = 0;
const clientSecret = "0";
const callbackURL = "http://localhost:3000/login/callback";
const weatherKey = "YourDarkSkyWeatherKey";
const port = 3000;
const accessToken = "0";
const mlEndpoint = "YourMLEndPoint";
const defaultExpirationTime = 7200; // Redis cache expiration time
const mongoDBUrl = ''; // MongoDB url
const dailyDarkSkyLimit = 500; // Hard limit on API calls per day
const elasticsearchendpoint = 'YourElasticsearchEndpoint';

exports.clientID = clientID;
exports.clientSecret = clientSecret;
exports.callbackURL = callbackURL;
exports.port = port;
exports.weatherKey = weatherKey;
exports.accessToken = accessToken;
exports.mlEndpoint = mlEndpoint;
exports.defaultExpirationTime = defaultExpirationTime;
exports.mongoDBUrl = mongoDBUrl;
exports.dailyDarkSkyLimit = dailyDarkSkyLimit;
exports.elasticsearchendpoint = elasticsearchendpoint;

Now setup a Redis instance and direct it to localhost with port 6379 (default configuration).

Install and run the machine learning server with Flask:

FLASK_APP=app.py flask run

To start the server use:

npm start

Running Tests

You can run tests using Mocha and Chai:

npm test

Contributing

Feel free to submit a pull request. The coding conventions of this app follow the Airbnb base style guide.