Module optimisation

The N-state model or structural ensemble analysis optimisation functions.

Functions

minimise_bc_data(model, sim_index=None)
Extract and unpack the back calculated data.

numpy rank-3 array, numpy rank-1 array. minimise_setup_atomic_pos(sim_index=None)
Set up the atomic position data structures for optimisation using PCSs and PREs as base data sets.

tuple of (list, numpy rank-1 array, numpy rank-1 array, numpy rank-1 array) minimise_setup_tensors(sim_index=None)
Set up the data structures for optimisation using alignment tensors as base data sets.

numpy rank-1 array. minimise_setup_fixed_tensors()
Set up the data structures for the fixed alignment tensors.

target_fn_setup(sim_index=None, scaling_matrix=None, verbosity=0)
Initialise the target function for optimisation or direct calculation.

Variables

__package__ = 'specific_analyses.n_state_model'

Imports: array, dot, float64, ones, zeros, inv, fix_invalid, RelaxError, RelaxNoModelError, align_tensor, opt_uses_align_data, opt_uses_tensor, interatomic_loop, return_spin, spin_loop, return_pcs_data, check_rdcs, return_rdc_data, base_data_types, tensor_loop, assemble_param_vector, update_model, N_state_opt

Function Details

minimise_bc_data(model, sim_index=None)

Extract and unpack the back calculated data.

Parameters:

• model (class instance) - The instantiated class containing the target function.
• sim_index (None or int) - The optional Monte Carlo simulation index.

minimise_setup_atomic_pos(sim_index=None)

Set up the atomic position data structures for optimisation using PCSs and PREs as base data sets.

Parameters:

• sim_index (None or int) - The index of the simulation to optimise. This should be None if normal optimisation is desired.

Returns: numpy rank-3 array, numpy rank-1 array.

The atomic positions (the first index is the spins, the second is the structures, and the third is the atomic coordinates) and the paramagnetic centre.

minimise_setup_tensors(sim_index=None)

Set up the data structures for optimisation using alignment tensors as base data sets.

Parameters:

• sim_index (None or int) - The index of the simulation to optimise. This should be None if normal optimisation is desired.

Returns: tuple of (list, numpy rank-1 array, numpy rank-1 array, numpy rank-1 array)

The assembled data structures for using alignment tensors as the base data for optimisation. These include:

• full_tensors, the data of the full alignment tensors.
• red_tensor_elem, the tensors as concatenated rank-1 5D arrays.
• red_tensor_err, the tensor errors as concatenated rank-1 5D arrays.
• full_in_ref_frame, flags specifying if the tensor in the reference frame is the full or reduced tensor.

minimise_setup_fixed_tensors()

Set up the data structures for the fixed alignment tensors.

Returns: numpy rank-1 array.

The assembled data structures for the fixed alignment tensors.

target_fn_setup(sim_index=None, scaling_matrix=None, verbosity=0)

Initialise the target function for optimisation or direct calculation.

Parameters:

• sim_index (None or int) - The index of the simulation to optimise. This should be None if normal optimisation is desired.
• scaling_matrix (numpy rank-2, float64 array or None) - The diagonal and square scaling matrix.
• verbosity (int) - A flag specifying the amount of information to print. The higher the value, the greater the verbosity.