relax SVN archive

Standardisation of the specific analysis API with respect to the model_loop() and base_data_loop().

The model information arguments for the data returned by model_loop(), and the data arguments for
the data arguments for the data returned by base_data_loop() have been standardised throughout the
API.

Created the consistency testing specific API method get_param_names().

This is now required for the minimise.calculate user function, specifically for the analysis
independent assemble_scaling_matrix() function. The get_param_names() method simply returns the
fixed list of parameter names.

Converted the relaxation curve-fitting analysis to the new grid bounds and scaling factor design.

The parameter object now registers the grid bounds and scaling factors for all of the curve-fitting
parameters. This includes three functions i_scaling(), i0() and iinf() in the
specific_analyses.relax_fit.parameter_object module for calculating some of these values.

The specific_analyses.relax_fit.parameters.assemble_scaling_matrix() function has been deleted as
this is now provided by the upstream code in pipe_control.minimise. And the API methods
grid_search() and minimise() has been modified to accept the list of scaling matrices.

As the grid bounds and increments are now handled by the upstream pipe_control.minimise.grid_setup()
function, the specific_analyses.relax_fit.optimisation.grid_search_setup() function was redundant
and was deleted.

Changed the order of operations in the minimisation user function backends.

The specific analysis API overfit_deselect() method needs to be called before any grid bounds,
increments, or the scaling matrices are assembled. This is for the cases when the grid bounds or
scaling factors are functions rather than values.

Modified the analysis specific API optimisation method.

The base calculate(), grid_search() and minimise() methods now all accept the scaling_matrix
argument, and the minimise() scaling argument has been removed. This scaling_matrix argument should
be a per-model list of scaling matrices.

To handle the change, the pipe_control.minimise.assemble_scaling_matrix() function has been created.
This uses the new parameter object scaling values to create the list of scaling matrices. This will
in the end replace all of the analysis specific assemble_scaling_matrix() functions and simplify
their optimisation code paths.

Created the parameter object infrastructure for registering parameter scalings.

The _add() method now accepts the scaling keyword argument, which can be either a value or function.
This is then stored in the _scaling dictionary. The public method scaling() has been added to
return the scaling factor corresponding to the given parameter.

More modifications of the minimise.grid_search user function backend.

The grid_bounds() function has been renamed to grid_setup(), and it now accepts and processes the
inc user function argument. The error checking code of the relaxation curve-fitting
grid_search_setup() optimisation function has been shifted into this analysis independent
grid_setup() function to shift the minimise.grid_search user function error checking out of the
specific analyses. The function now scales the parameter bounds, using the yet-to-be implemented
scaling() method of the parameter object. And the grid search increments are converted into a
per-model list of lists.

Implemented infrastructure in the grid_search user function in preparation for the zooming grid.

The grid search backend now calls the new grid_bounds() function. This takes the lower and upper
bounds as arguments, uses the specific API to determine the per-model parameter grid search bounds,
and then returns a per model list of lower and upper bounds. The specific API get_param_names() and
get_param_values() are called to obtain the current model parameter names and values, and then the
parameter names and model info are used in the new parameter object grid_lower() and grid_upper()
methods to obtain the bounds. This shifts all of the grid search bounds logic out of the specific
analyses and into the grid search backend, so it should allow the specific analysis code to be
simplified.

Modified the specific analysis parameter object grid_lower() and grid_upper() methods.

These now accept the model information from the model_loop() API method and send that into an grid
lower and upper functions. These functions will require the information to pull out the correct
spin, spin cluster, or other information from the current data pipe to determine what the bounds
should be.

Created the parameter object infrastructure for adding the grid search lower and upper bounds.

The _add() method now accepts the grid_lower and grid_upper keyword arguments, which can be either
values or functions. These are then stored in the _grid_lower and _grid_upper class dictionaries.
The public methods grid_lower() and grid_upper() have been added to return the value corresponding
to the given parameter.