relax SVN archive

The weights of each state are now set to equal probability prior to the 'population' opt.

This is in the 'population' N-state model system test script.

Added the pymol_macro method to the specific object API.

Removed the calls to rdc.display() and pcs.display().

These were rightfully failing for the empty data lists, so should not be tested.

Modified the RDC and PCS read() function to not add align_id to the cdp structures if no data is present.

Created a system test for the 'population' N-state model with missing data.

This should thoroughly test out this model! First the 'fixed' model is optimised, and then the
'population' model optimised from that starting point. This is to keep the test fast.

Implemented dfunc_tensor_opt() and d2func_tensor_opt() allowing all optimisation algorithms to be used.

The 'fixed' N-state model can now be optimised using Newton optimisation. On my machine, the
performance vs. Nelder-Mead simplex for ~20 states is a drop from ~1 hour to less than one minute!

Forked the test_lactose_n_state() system test for both the 'population' and 'fixed' models.

The 2 new system tests are test_lactose_n_state_fixed() and test_lactose_n_state_population().

Fixed the Q-factor calculating functions to handle missing RDC or PCS lists.

The n_state_model.select_model() user function can now change the model.

Previously a RelaxError was thrown, but now a warning is given that the model is changing. This
allows the 'population' N-state model to be optimised by first optimising the 'fixed' model and then
switching to 'population'.

Big bug fixes for the _minimise_setup_pcs() and _minimise_setup_rdcs() methods.

This allows the missing data PCS and RDC optimisation system test to pass. Many problems were
solved to allow all types of data with anything missing (one spin in one alignment, one spin in all
RDC or all PCS lists, or entire RDC or PCS lists for one alignment).