Turn the Monte Carlo simulations off.
This will turn off the Monte Carlo simulations so that subsequent optimisation will operate directly on the model parameters and not on the simulations.
For proper error analysis using Monte Carlo simulations, a sequence of function calls is required for running the various simulation components. The steps necessary for implementing Monte Carlo simulations are:
Monte Carlo simulations can be turned on or off using functions within this class. Once the function for setting up simulations has been called, simulations will be turned on. The effect of having simulations turned on is that the functions used for minimisation (grid search, minimise, etc) or calculation will only affect the simulation parameters and not the model parameters. By subsequently turning simulations off using the appropriate function, the functions used in minimisation will affect the model parameters and not the simulation parameters.
An example for model-free analysis using the prompt UI mode which includes only the functions required for implementing the above steps is:
[numbers=none] relax> minimise.grid_search(inc=11) # Step 2.
[numbers=none] relax> minimise.execute('newton') # Step 2.
[numbers=none] relax> monte_carlo.setup(number=500) # Step 3.
[numbers=none] relax> monte_carlo.create_data(method='back_calc') # Step 4.
[numbers=none] relax> monte_carlo.initial_values() # Step 5.
[numbers=none] relax> minimise.execute('newton') # Step 6.
[numbers=none] relax> eliminate() # Step 7.
[numbers=none] relax> monte_carlo.error_analysis() # Step 8.
An example for reduced spectral density mapping is:
[numbers=none] relax> minimise.calculate() # Step 2.
[numbers=none] relax> monte_carlo.setup(number=500) # Step 3.
[numbers=none] relax> monte_carlo.create_data(method='back_calc') # Step 4.
[numbers=none] relax> minimise.calculate() # Step 6.
[numbers=none] relax> monte_carlo.error_analysis() # Step 8.