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Classes for Gaussian Process Regression fitting of 1D data with errorbars.
pip install GPR1D==1.3.1
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:
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:
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 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
For those who wish to include the functionality of this package into their own Python scripts, a demo script is provided in scripts/. The basic syntax used to create kernels, select settings, and perform GPR 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:
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.
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