Fall 2014 NuPIC Hackathon
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


Shake Hack

Fall 2014 NuPIC Hackathon

Earthquakes in California

  • Colors map to anomaly score
    • Red: High anomaly score
    • Green: Low anomaly score
  • Size of dot maps to magnitude
  • 10 years of earthquake data (lat, long, magnitude) for the 1000km radius surrounding the Pinger, In. headquarters in San Jose, CA.
  • Used coordinate encoder

See the video!



  • Start redis

    $ redis-server
  • Start run.py

    $ python run.py
  • Start webapp

    $ cd webapp
    $ API_KEY=<insert API key here> python serve.py
  • Open http://localhost:8080 in browser

Usage (Vagrant + CoreOS + Docker)

Download and install Docker client, Virtualbox, and Vagrant, and then:

source env.sh
vagrant up
docker build -t shakehack .
docker run --name shakehack-redis -d -p redis
docker run \
  --name shakehack-server \
  --link shakehack-redis:broker \
  -e REDIS_HOST=broker \
  -d \
  -p \
  -w /opt/numenta/shakehack/webapp \
  shakehack \
  python serve.py
docker run \
  --link shakehack-redis:broker \
  -e REDIS_HOST=broker \
  shakehack \
  python run.py

Redis, the shakehack web service, and the shakehack entry point are now running in separate containers in a vm. You should be able to access the web service in a browser at http://localhost:8080


This project is a work in progress. The initial mechanics of running data through NuPIC and presenting it to the user are there, but there's still much left to do to add value. Here's a sampling of some ideas:

  • Create encoding scheme that better represents the magnitude of an earthquake event. Currently using the coordinate encoder as-is, mapping magnitude to radius one-to-one, but there's likely a better encoding scheme that takes into account the logarithmic scale of the magnitude. May also be some benefit to incorporating depth into the model.
  • Make use of anomaly likelihood algorithm to classify events and/or incorporate additional traditional statistical models
  • Automated cluster classification
  • Additional user interactivity to explore events and replay periods of time
  • Split data into training and test data based on variety of factors (bounding box, time, magnitude, etc.), save model on trained data set, load and replay new data
  • Add a listener to http://earthquake.usgs.gov/earthquakes/feed/v1.0/geojson.php for real-time(ish) updates
  • Display only a buffer of recent events rather than all accumulated. Maybe make the buffer size dynamic based on moving average.