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Python package for data-consistent stochastic inverse and forward problems.

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UT-CHG/BET

BET

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BET is a Python package for data-consistent stochastic forward and inverse problems. The package is very flexible and is applicable to a wide variety of problems.

BET is an initialism of Butler, Estep and Tavener, the primary authors of a series of papers that introduced the mathematical framework for measure-based data-consistent stochastic inversion, for which BET included a computational implementation. However, since its initial inception it has grown to include a broad range of data-consistent methods. It has been applied to a wide variety of application problems, many of which can be found here.

Installation

The current development branch of BET can be installed from GitHub, using pip:

pip install git+https://github.com/UT-CHG/BET

Another option is to clone the repository and install BET using python setup.py install

Dependencies

BET is tested on Python 3.6, 3.7, and 3.8 (but should work on most recent Python 3 versions) and depends on NumPy, SciPy, matplotlib, pyDOE, pytest, and mpi4py (optional) (see requirements.txt for version information). For some optional features LUQ is also required. mpi4py is required to take advantage of parallel features and requires an mpi implementation. It can be installed by:

pip install mpi4py

License

GNU Lesser General Public License (LGPL)

Citing BET

Please include the citation:

Lindley Graham, Steven Mattis, Michael Pilosov, Scott Walsh, Troy Butler, Michael Pilosov, … Damon McDougall. (2020, July 9). UT-CHG/BET: BET v3.0.0 (Version v3.0.0). Zenodo. http://doi.org/10.5281/zenodo.3936258

or in BibTEX:

@software{BET,
          author = {Lindley Graham and
                    Steven Mattis and
                    Michael Pilosov and
                    Scott Walsh and
                    Troy Butler and
                    Wenjuan Zhang and
                    Damon McDougall},
          title = {UT-CHG/BET: BET v3.0.0},
          month = jul,
          year = 2020,
          publisher = {Zenodo},
          version = {v3.0.0},
          doi = {10.5281/zenodo.3936258},
          url = {https://doi.org/10.5281/zenodo.3936258}
          }

Documentation

This code has been documented with sphinx. the documentation is available online at http://ut-chg.github.io/BET. To build documentation run make html in the doc/ folder.

To build/update the documentation use the following commands:

sphinx-apidoc -f -o doc bet
cd doc/
make html
make html

This creates the relevant documentation at bet/gh-pages/html. To change the build location of the documentation you will need to update doc/makefile.

You will need to run sphinx-apidoc and reinstall bet anytime a new module or method in the source code has been added. If only the *.rst files have changed then you can simply run make html twice in the doc folder. Building the docs requires Sphinx and the Read the Docs Sphinx theme, which can be installed with pip by:

pip install Sphinx sphinx_rtd_theme

Examples

Examples scripts are contained in here.

You can also try out BET in your browser using Binder.

Testing

To run the tests in the root directory with pytest in serial call:

pytest ./test/

Some features of BET (primarily those associated with the measure-based approach) have the ability to work in parallel. To run tests in parallel call:

mpirun -np NPROC pytest ./test/

Make sure to have a working MPI environment (we recommend mpich) if you want to use parallel features.

(Note: you may need to set ~/.config/matplotlib/matplotlibrc to include backend:agg if there is no DISPLAY port in your environment).

Contributors

See the GitHub contributors page.

Contact

BET is in active development. Hence, some features are still being added and you may find bugs we have overlooked. If you find something please report these problems to us through GitHub so that we can fix them. Thanks!

Please note that we are using continuous integration and issues for bug tracking.

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Python package for data-consistent stochastic inverse and forward problems.

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License

Unknown and 2 other licenses found

Licenses found

Unknown
LICENSE.txt
GPL-3.0
COPYING
LGPL-3.0
COPYING.LESSER

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