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BATMAN: Statistical analysis for expensive computer codes made easy

Project description

CI Codecov Python License Conda PyPi Zulip Joss

Batman

Batman stands for Bayesian Analysis Tool for Modelling and uncertAinty quaNtification. It is a Python module distributed under the open-source CECILL-B license (MIT/BSD compatible).

batman seamlessly allows to do statistical analysis (sensitivity analysis, Uncertainty Quantification, moments) based on non-intrusive ensemble experiment using any computer solver. It relies on open source python packages dedicated to statistics (OpenTURNS and scikit-learn).

Main features are:

  • Design of Experiment (LHS, low discrepancy sequences, MC),
  • Resample the parameter space based on the physic and the sample,
  • Surrogate Models (Gaussian process, Polynomial Chaos, RBF, scikit-learn’s regressors),
  • Optimization (Expected Improvement),
  • Sensitivity/Uncertainty Analysis (SA, UA) and Uncertainty Quantification (UQ),
  • Visualization in n-dimensions (HDR, Kiviat, PDF),
  • POD for database optimization or data reduction,
  • Automatically manage code computations in parallel.

Full documentation is available at:

http://batman.readthedocs.io

Getting started

A detailled example can be found in tutorial. The folder test_cases contains examples that you can adapt to you needs. You can find more information about the cases within the respectives README.rst file.

Shoud you be interested by batman’s implementation, consider reading the technical documentation.

If you encounter a bug (or have a feature request), please report it via GitLab. Or it might be you falling but “Why do we fall sir? So we can learn to pick ourselves up”.

Last but not least, if you consider contributing check-out contributing.

Happy batman.

How to install BATMAN?

The sources are located on GitLab:

https://gitlab.com/cerfacs/batman

Latest release

Install and update using pip:

pip install -U ot-batman

batman is also distributed through conda, on the conda-forge channel.

To install conda:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

To create a new environment and install batman through conda:

conda create -n bat_env -c conda-forge batman

All the above dependencies are automatically handled by conda, except Antares. For more information, refer to its documentation.

From sources

Using the latest python version is prefered! Then to install:

git clone git@gitlab.com:cerfacs/batman.git
cd batman
python setup.py install
python setup.py test
python setup.py build_sphinx

The latter is optionnal as it build the documentation. The testing part is also optionnal but is recommanded. (<30mins depending on your configuration).

Note

If you don’t have install priviledge, add --user option after install. But the simplest way might be to use pip or a conda environment.

If batman has been correctly installed, you should be able to call it simply:

batman -h

Warning

Depending on your configuration, you might have to export your local path: export PATH=$PATH:~/.local/bin. Care to be taken with both your PATH and PYTHONPATH environment variables. Make sure you do not call different installation folders. It is recommanded that you leave your PYTHONPATH empty.

Dependencies

The required dependencies are:

Appart from OpenTURNS, required dependencies are satisfied by the installer. Optionnal dependencies are:

  • Antares for extra IO options
  • sphinx >= 1.4 for documentation
  • ffmpeg for movie visualizations (n_features > 2)

Testing dependencies are:

Extra testing flavours:

Note

OpenTURNS and ffmpeg are available on conda through the conda-forge channel.

Help and Support

About us

See authors and project history at: about us.

Community

If you use batman, come and say hi at https://batman-cerfacs.zulipchat.com. Or send us an email. We would really appreciate that as we keep record of the users!

Citation

If you use batman in a scientific publication, we would appreciate citations.

Project details


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This version

1.9.1

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