Metadata-Version: 2.4
Name: fvgp
Version: 4.6.8
Summary: Python package for highly flexible function-valued Gaussian processes (fvGP)
Project-URL: Homepage, https://github.com/lbl-camera/fvgp
Project-URL: Documentation, https://fvgp.readthedocs.io/
Project-URL: Repository, https://github.com/lbl-camera/fvgp.git
Project-URL: Bug Tracker, https://github.com/lbl-camera/fvgp/issues
Project-URL: Changelog, https://github.com/lbl-camera/fvgp/commits/master/
Author-email: Marcus Michael Noack <MarcusNoack@lbl.gov>, "Ronald J. Pandolfi" <ronpandolfi@lbl.gov>
Maintainer-email: Marcus Michael Noack <MarcusNoack@lbl.gov>, "Ronald J. Pandolfi" <ronpandolfi@lbl.gov>
License: *** License Agreement ***
        
        GPL v3 License
        
        fvGP Copyright (c) 2021, The Regents of the University of California,
        through Lawrence Berkeley National Laboratory (subject to receipt of
        any required approvals from the U.S. Dept. of Energy). All rights reserved.
        
        This program is free software: you can redistribute it and/or modify
        it under the terms of the GNU General Public License as published by
        the Free Software Foundation, either version 3 of the License, or
        (at your option) any later version.
        
        This program is distributed in the hope that it will be useful,
        but WITHOUT ANY WARRANTY; without even the implied warranty of
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
        GNU General Public License for more details.
        
        You should have received a copy of the GNU General Public License
        along with this program.  If not, see <https://www.gnu.org/licenses/>.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
        ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
        LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
        TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
        THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
License-File: AUTHORS.rst
License-File: COPYING
License-File: LICENSE
Keywords: adaptive,autonomous,gui,qt,self driving
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Requires-Dist: dask~=2025.5.1
Requires-Dist: distributed~=2025.5.1
Requires-Dist: hgdl~=2.3.1
Requires-Dist: loguru
Requires-Dist: numpy~=2.2.6
Requires-Dist: scipy~=1.16.0
Requires-Dist: wheel
Provides-Extra: docs
Requires-Dist: autodocs; extra == 'docs'
Requires-Dist: jupytext; extra == 'docs'
Requires-Dist: myst-nb; extra == 'docs'
Requires-Dist: myst-parser; extra == 'docs'
Requires-Dist: sphinx; extra == 'docs'
Requires-Dist: sphinx-panels; extra == 'docs'
Requires-Dist: sphinx-rtd-theme; extra == 'docs'
Provides-Extra: plotting
Requires-Dist: matplotlib; extra == 'plotting'
Requires-Dist: notebook; extra == 'plotting'
Requires-Dist: plotly; extra == 'plotting'
Provides-Extra: tests
Requires-Dist: codecov; extra == 'tests'
Requires-Dist: imate; extra == 'tests'
Requires-Dist: pytest; extra == 'tests'
Requires-Dist: pytest-cov; extra == 'tests'
Requires-Dist: torch; extra == 'tests'
Description-Content-Type: text/markdown

# fvGP

[![PyPI](https://img.shields.io/pypi/v/fvGP)](https://pypi.org/project/fvgp/)
[![Documentation Status](https://readthedocs.org/projects/fvgp/badge/?version=latest)](https://fvgp.readthedocs.io/en/latest/?badge=latest)
[![fvGP CI](https://github.com/lbl-camera/fvGP/actions/workflows/fvGP-CI.yml/badge.svg)](https://github.com/lbl-camera/fvGP/actions/workflows/fvGP-CI.yml)
[![Codecov](https://img.shields.io/codecov/c/github/lbl-camera/fvGP)](https://app.codecov.io/gh/lbl-camera/fvGP)
[![PyPI - License](https://img.shields.io/badge/license-GPL%20v3-lightgrey)](https://pypi.org/project/fvgp/)
[<img src="https://img.shields.io/badge/slack-@gpCAM-purple.svg?logo=slack">](https://gpCAM.slack.com/)
[![DOI](https://zenodo.org/badge/434769505.svg)](https://zenodo.org/badge/latestdoi/434769505)


Python package for highly flexible function-valued Gaussian processes (fvGP)

It is recommended to use this package via [gpCAM](https://gpcam.lbl.gov/).

Specialties: Extreme-Scale GPs, GPs Tailored for HPC training, Advanced Kernel Designs, Domain-Aware Stochastic Function Approximation

Coming soon: All those capabilitites for stochastic manifold learning

fvGP holds the world record for exact large-scale Gaussian Processes!
## Credits

This package uses the HGDL package of David Perryman and Marcus Noack, which
is based on the [HGDN](https://www.sciencedirect.com/science/article/pii/S037704271730225X) algorithm by Noack and Funke.
