bumps 0.7.5.4

Data fitting with bayesian uncertainty analysis

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

Relaase notes

v0.7.5.4 2014-12-05

• 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

v0.7.5.3 2014-11-21

• use –time to stop after a given number of hours

• Levenberg-Marquardt: fix “must be 1-d or 2-d” bug

• improve curvefit interface

v0.7.5.2 2014-09-26

• pull numdifftools dependency into the repository

v0.7.5.1 2014-09-25

• improve the load_model interface

v0.7.5 2014-09-10

• Pure python release