Module optimisation

The model-free analysis optimisation functions.

Classes

MF_memo
The model-free memo class.

MF_minimise_command
Command class for standard model-free minimisation.

MF_grid_command
Command class for the model-free grid search.

MF_result_command
Class for processing the model-free results.

Functions

disassemble_result(param_vector=None, func=None, iter=None, fc=None, gc=None, hc=None, warning=None, spin=None, sim_index=None, model_type=None, scaling=None, scaling_matrix=None)
Disassemble the optimisation results.

grid_search_config(num_params, spin=None, spin_id=None, lower=None, upper=None, inc=None, scaling_matrix=None, verbosity=1)
Configure the grid search.

grid_search_diff_bounds(lower, upper)
Set up the default grid search bounds the diffusion tensor.

grid_search_spin_bounds(spin, lower, upper)
Set up the default grid search bounds for a single spin.

tuple minimise_data_setup(data_store, min_algor, num_data_sets, min_options, spin=None, sim_index=None)
Set up all the data required for minimisation.

tuple relax_data_opt_structs(spin, sim_index=None)
Package the relaxation data into the data structures used for optimisation.

reset_min_stats()
Reset all the minimisation statistics.

spin_print(spin_id, verbosity)
Print out some header text for the spin.

Variables

__package__ = 'specific_analyses.model_free'

Imports: pi, generic_minimise, grid, grid_point_array, array, dot, float64, match, lib, RelaxError, RelaxInfError, RelaxMultiVectorError, RelaxNaNError, isNaN, isInf, test_grid_ops, return_gyromagnetic_ratio, Memo, Result_command, Slave_command, pipes, return_interatom_list, return_spin, return_spin_from_index, spin_loop, determine_model_type, assemble_param_vector, disassemble_param_vector, Mf

Function Details

disassemble_result(param_vector=None, func=None, iter=None, fc=None, gc=None, hc=None, warning=None, spin=None, sim_index=None, model_type=None, scaling=None, scaling_matrix=None)

Disassemble the optimisation results.

Parameters:

• param_vector (numpy array) - The model-free parameter vector.
• func (float) - The optimised chi-squared value.
• iter (int) - The number of optimisation steps required to find the minimum.
• fc (int) - The function count.
• gc (int) - The gradient count.
• hc (int) - The Hessian count.
• warning (str or None) - Any optimisation warnings.
• spin (SpinContainer instance or None) - The spin container.
• sim_index (int or None) - The Monte Carlo simulation index.
• model_type (str) - The model-free model type, one of 'mf', 'local_tm', 'diff', or 'all'.
• scaling (bool) - If True, diagonal scaling is enabled during optimisation to allow the problem to be better conditioned.
• scaling_matrix (numpy diagonal matrix) - The diagonal, square scaling matrix.

grid_search_config(num_params, spin=None, spin_id=None, lower=None, upper=None, inc=None, scaling_matrix=None, verbosity=1)

Configure the grid search.

Parameters:

• num_params (int) - The number of parameters in the model.
• spin (SpinContainer instance) - The spin data container.
• spin_id (str) - The spin identification string.
• lower (array of numbers) - The lower bounds of the grid search which must be equal to the number of parameters in the model.
• upper (array of numbers) - The upper bounds of the grid search which must be equal to the number of parameters in the model.
• inc (array of int) - The increments for each dimension of the space for the grid search. The number of elements in the array must equal to the number of parameters in the model.
• scaling_matrix (numpy diagonal matrix) - The diagonal, square scaling matrix.
• verbosity (int) - A flag specifying the amount of information to print. The higher the value, the greater the verbosity.

grid_search_diff_bounds(lower, upper)

Set up the default grid search bounds the diffusion tensor.

This method appends the default bounds to the lower and upper lists.

Parameters:

• lower (list) - The lower bound list to append to.
• upper (list) - The upper bound list to append to.

grid_search_spin_bounds(spin, lower, upper)

Set up the default grid search bounds for a single spin.

This method appends the default bounds to the lower and upper lists. The ordering of the lists in min_options matches that of the params list in the spin container.

Parameters:

• spin (class instance) - A SpinContainer object.
• lower (list) - The lower bound list to append to.
• upper (list) - The upper bound list to append to.

minimise_data_setup(data_store, min_algor, num_data_sets, min_options, spin=None, sim_index=None)

Set up all the data required for minimisation.

Parameters:

• data_store (class instance) - A data storage container.
• min_algor (str) - The minimisation algorithm to use.
• num_data_sets (int) - The number of data sets.
• min_options (list) - The minimisation options array.
• spin (SpinContainer instance) - The spin data container.
• sim_index (int) - The optional MC simulation index.

Returns: tuple

An insane tuple. The full tuple is (ri_data, ri_data_err, equations, param_types, param_values, r, csa, num_frq, frq, num_ri, remap_table, noe_r1_table, ri_types, num_params, xh_unit_vectors, diff_type, diff_params)

relax_data_opt_structs(spin, sim_index=None)

Package the relaxation data into the data structures used for optimisation.

Parameters:

• spin (SpinContainer instance) - The spin container to extract the data from.
• sim_index (int) - The optional MC simulation index.

Returns: tuple

The structures ri_data, ri_data_err, num_frq, num_ri, ri_ids, frq, remap_table, noe_r1_table.

reset_min_stats()

Reset all the minimisation statistics.

All global and spin specific values will be set to None.

spin_print(spin_id, verbosity)

Print out some header text for the spin.

Parameters:

• spin_id (str) - The spin ID string.
• verbosity (int) - The amount of information to print. The higher the value, the greater the verbosity.