Metadata-Version: 2.1
Name: geobipy
Version: 2.2.1
Summary: Markov chain Monte Carlo inversion
Home-page: https://github.com/DOI-USGS/geobipy
Author: Leon Foks
Author-email: nfoks@contractor.usgs.gov
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved
Classifier: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
License-File: LICENSE-BSD.md
License-File: LICENSE-GPLv2.md
License-File: LICENSE.md
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: h5py
Requires-Dist: numba
Requires-Dist: pandas
Requires-Dist: netcdf4
Requires-Dist: matplotlib
Requires-Dist: pyvista
Requires-Dist: sphinx
Requires-Dist: progressbar2
Requires-Dist: cached-property
Requires-Dist: empymod
Requires-Dist: smm
Requires-Dist: lmfit
Requires-Dist: scikit-learn
Requires-Dist: randomgen
Requires-Dist: numba-kdtree
Requires-Dist: pygmt

############################################################
Welcome to GeoBIPy: Geophysical Bayesian Inference in Python
############################################################

This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
derive posterior distributions of subsurface and measured data properties.
The current implementation is applied to time and frequency domain electromagnetic data.
Application outside of these data types is in development.

Citation
~~~~~~~~

Foks, N. L., and Minsley, B. J. 2020. GeoBIPy - Geophysical Bayesian Inference in Python. 10.5066/P9K3YH9O

Background scientific references
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Minsley, B. J., Foks, N. L., and Bedrosian, P. A. 2020. Quantifying model structural uncertainty using airborne electromagnetic data. Geophys. J. Int. 224, 1, 590–607. https://doi.org/10.1093/gji/ggaa393

Minsley, B. J. 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophys. J. Int. 187, 252–272. 10.1111/j.1365-246X.2011.05165.x

`Documentation is here! <https://doi-usgs.github.io/geobipy/>`_

This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.
