Package specific_analyses :: Package n_state_model :: Module optimisation
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Module optimisation

source code

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

Functions [hide private]
 
minimise_bc_data(model)
Extract and unpack the back calculated data.
source code
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.
source code
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.
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numpy rank-1 array.
minimise_setup_fixed_tensors()
Set up the data structures for the fixed alignment tensors.
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target_fn_setup(sim_index=None, scaling=True)
Initialise the target function for optimisation or direct calculation.
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Variables [hide private]
  __package__ = 'specific_analyses.n_state_model'

Imports: array, dot, float64, ones, zeros, inv, 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, assemble_scaling_matrix, update_model, N_state_opt


Function Details [hide private]

minimise_bc_data(model)

source code 

Extract and unpack the back calculated data.

Parameters:
  • model (class instance) - The instantiated class containing the target function.

minimise_setup_atomic_pos(sim_index=None)

source code 

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)

source code 

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()

source code 

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=True)

source code 

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 (bool) - If True, diagonal scaling is enabled during optimisation to allow the problem to be better conditioned.