Module optimisation
source code
The N-state model or structural ensemble analysis optimisation
functions.
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numpy rank-3 array, numpy rank-1 array.
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tuple of (list, numpy rank-1 array, numpy rank-1 array, numpy rank-1
array)
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numpy rank-1 array.
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target_fn_setup(sim_index=None,
scaling_matrix=None,
verbosity=0)
Initialise the target function for optimisation or direct
calculation. |
source code
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__package__ = ' specific_analyses.n_state_model '
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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
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.
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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.
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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:
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full_tensors, the data of the full alignment tensors.
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red_tensor_elem, the tensors as concatenated rank-1 5D
arrays.
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red_tensor_err, the tensor errors as concatenated rank-1 5D
arrays.
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full_in_ref_frame, flags specifying if the tensor in the
reference frame is the full or reduced tensor.
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Set up the data structures for the fixed alignment tensors.
- Returns: numpy rank-1 array.
- The assembled data structures for the fixed alignment tensors.
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target_fn_setup(sim_index=None,
scaling_matrix=None,
verbosity=0)
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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.
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