relax

This is a minor bugfix release that resolves an important issue, introduced somewhere within the last few relax releases, whereby missing relaxation data is not handled properly in a model-free analysis. A number of other small bugs have also been fixed.

This release solved a number of issues, including Python 3 issues, PDB and structure handling, and missing relaxation data causing model-free analyses to fail. Upgrading to this newest version is highly recommended.

This is a major feature and bugfix release that marks the completion of the N-state model analysis implementation. The N-state model allows single structures or ensembles of static structures to be analyzed and compared using residual dipolar couplings (RDCs), pseudo-contact shifts (PCSs), and distance restraints via NOEs. In addition to alignment tensors, the populations or probabilities of each state can be optimized, as well as the position of the paramagnetic center when PCSs are used.

This is the second version of the relax GUI, and is a major code rewrite. It now functional on GNU/Linux, Mac OS X, and MS Windows. In addition to many bugfixes, there are improvements to the frame order theory, N-state model analysis, handling of RDC and PCS values, a number of new user functions for structure creation, displacement, and superimposition and for model-free result visualisation in PyMOL, and a redesign of the auto-analyses to have all input data pre-loaded into a relax data pipe for support of non-protein organic molecules in the dauvergne_protocol auto-analysis.

This is a major feature release. It adds the ability to run relax on clusters or grids of computers via the MPI protocol. This merges in Gary Thompson's multi-processor branch, which was started all the way back in 2007. The "multi" package introduces two processor fabrics, the standard uni-processor mode and the mpi4py mode for using the MPI protocol with Python. The model-free analysis code has been parallelized to take advantage of the multi-processor modes, significantly speeding up calculations on clusters with near perfect scaling efficiency.