Metadata-Version: 1.1
Name: GPR1D
Version: 1.2.1
Summary: Classes for Gaussian Process Regression fitting of 1D data with errorbars.
Home-page: https://gitlab.com/aaronkho/GPR1D.git
Author: Aaron Ho
Author-email: a.ho@differ.nl
License: MIT
Description: 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 :code:`--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 :code:`[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 <https://aaronkho.gitlab.io/GPR1D>`_
        
        
        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 R\ :sup:`2` and pseudo R\ :sup:`2`
        metrics are now available. The adjusted R\ :sup:`2` metric provides
        a measure of how close the fit is to the input data points. The
        pseudo R\ :sup:`2` provides a measure of this closeness accounting
        for the input data uncertainties.
        
Keywords: gaussian process regression,1D data fitting,regression analysis,kriging
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
