# Hot Gym Code Example

The ["hot gym"](https://github.com/numenta/nupic/tree/master/examples/opf/clients/hotgym) sample application has been around for a long time, and was one of the first real-world applications of NuPIC that proved the value of cortically-inspired learning algorithms. The data used is real energy consumption data from a gym in Australia, which simply contains a timestamp and float value for energy consumption.

The "hot gym" sample application has been around for a long time, and was one of the first real-world applications of NuPIC that actually worked. The data used is real energy consumption data from a gym in Australia. It is aggregated hourly already, so the input CSV file simply contains a timestamp and float value for energy consumption during that hour.

This collection of tutorials uses the "Hot Gym" premise to illustrate many ways users can set up and run a NuPIC application against real-world data.

- [Basic Anomaly Client through OPF](https://github.com/numenta/nupic/tree/master/examples/opf/clients/hotgym/anomaly)
- [Prediction with OPF](https://github.com/numenta/nupic/tree/master/examples/opf/clients/hotgym/prediction/one_gym)
- [Anomaly Detection with OPF](https://github.com/numenta/nupic/tree/master/examples/opf/clients/hotgym/anomaly/one_gym)