Classes for Gaussian Process Regression fitting of 1D data with errorbars.
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]
--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 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 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:
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.
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