Data fitting with bayesian uncertainty analysis
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Bumps provides data fitting and Bayesian uncertainty modeling for inverse problems. It has a variety of optimization algorithms available for locating the most like value for function parameters given data, and for exploring the uncertainty around the minimum.
Installation is with the usual python installation command:
pip install bumps
Once the system is installed, you can verify that it is working with:
bumps doc/examples/peaks/model.py --chisq bumps -h
To start the GUI use:
bumps
Documentation is available at readthedocs. See CHANGES.rst for details on recent changes.
If a compiler is available, then significant speedup is possible for DREAM using:
python -m bumps.dream.build_compiled
If you have installed from source, you must first check out the random123 library:
git clone --branch v1.14.0 https://github.com/DEShawResearch/random123.git bumps/dream/random123 python -m bumps.dream.build_compiled
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