Based on GaPP, this fork aims to provide native execution of the library in Python 3 environments. Its utility lies in the incompatibilities of the original due to API changes in dependencies like NumPy, and the emergence of new platforms like aarch64-apple-darwin which are not natively supported by Python 2.
To install NeoGaPP, simply clone this repository into a local directory, and install it by running pip install . in the root of the same directory.
The
examplesfolder contains some sample programs to demonstrate the capabilities of the library.
GaPP, meaning Gaussian Processes in Python, is a library that facilitates the use of Gaussian processes, which can be used to reconstruct a function from a sample of data without assuming a parameterization of it. It handles individual error bars on the data and can be used to determine the derivatives of the reconstructed function.
Credit is given to the original authors of GaPP which was written by Marina Seikel, Chris Clarkson, and Mathew Smith, and used in their paper Reconstruction of dark energy and expansion dynamics using Gaussian processes.