gpr1d 2.0.0

Classes for Gaussian Process Regression fitting of 1D data with errorbars

gpr1d

Installing the gpr1d program

Author: Aaron Ho (01/06/2018)

Installation is mandatory for this package!

For first time users, it is strongly recommended to use the GUI developed for this Python package. To obtain the Python package dependencies needed to use this capability, install this package by using the following on the command line:

pip install [--user] gpr1d[guis]

Use the --user flag if you do not have root access on the system that you are working on. If you have already cloned the repository, enter the top level of the repository directory and use the following instead:

pip install [--user] -e .[guis]

Removal of the [guis] portion will no longer check for the GUI generation and plotting packages needed for this functionality. However, these packages are not crucial for the base classes and algorithms.

Documentation

Documentation of the equations used in the algorithm, along with the available kernels and optimizers, can be found in docs/. Documentation of the gpr1d module can be found on GitLab pages

Using the gpr1d program

For those who wish to include the functionality of this package into their own Python scripts, a demo script is provided in src/gpr1d/scripts. The basic syntax used to create kernels, select settings, and perform GP regression fits are outlined there.

In addition, a simplified gpr1d class is available for those wishing to distill the parameters into a subset of the most crucial ones.

For any questions or to report bugs, please do so through the proper channels in the GitLab repository.

Important note for users!

The following runtime warnings are common within this routine, but they are filtered out by default:

RuntimeWarning: overflow encountered in double_scalars
RuntimeWarning: invalid value encountered in true_divide
RuntimeWarning: invalid value encountered in sqrt

They normally occur when using the kernel restarts option (as in the demo) and do not necessarily mean that the resulting fit is poor.

Plotting the resulting fit and errors is the recommended way to check its quality. The log-marginal-likelihood metric can also be used, but is only valuable when comparing different fits of the same data, ie. its absolute value is meaningless.

From v1.1.1, the adjusted R² and pseudo R² metrics are now available. The adjusted R² metric provides a measure of how close the fit is to the input data points. The pseudo R² provides a measure of this closeness accounting for the input data uncertainties.