In this post…
I describe a few data structures that are useful in distributed systems.
Registration of two images using scikit image.
In this example I am going to explain how to detect a type of anomaly in a time series. The time series is composed by a slow varying background signal with gaussian noise on top of which we simulate a anomaly defined as a set of continuous values above the average.
A custom Jupyter kernel allows for user customization of packages and settings loaded at startup so that we do not have to start all notebooks with the same setup code.
In this post I describe an alternative method of querying the ESO archive using browser automation. The method is simple and general enough to be applicable to any other archive web interface.
This is a quick example on using TensorFlow to learn about a time series and perform forecasting. This can be used as an entry point for detecting anomalies.