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:

python setup.py install

This installs the package for all users of the system. To isolate the package it is useful to install virtualenv and virtualenv-wrapper.

This allows you to say:

mkvirtualenv --system-site-packages bumps python setup.py develop

Once the system is installed, you can verify that it is working with:

bumps doc/examples/peaks/model.py --chisq

Documentation is available at readthedocs

• tweak uncertainty calculations so they don't fail on bad models

• documentation updates

• use relative rather than absolute noise in dream, which lets us fit target values in the order of 1e-6 or less.
• fix covariance population initializer

• use --time to stop after a given number of hours
• Levenberg-Marquardt: fix "must be 1-d or 2-d" bug
• improve curvefit interface

• pull numdifftools dependency into the repository

• improve the load_model interface

• Pure python release