BATMAN: Statistical analysis for expensive computer codes made easy
Project description
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:
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:
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).
If batman has been correctly installed, you should be able to call it simply:
batman -h
Dependencies
The required dependencies are:
Python >= 2.7 or >= 3.4
OpenTURNS >= 1.10
scikit-learn >= 0.18
numpy >= 1.13
scipy >= 0.15
pathos >= 0.2
matplotlib >= 2.1
Paramiko >= 2.4
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:
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.
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