sme_contrib.optimize.SteadyState.find

SteadyState.find(particles=20, iterations=20, processes=None)

Find parameters that result in a steady state concentration close to the target image

Uses particle swarm to minimize the difference between the rescaled concentration and the target image, as well as the distance from a steady state solution.

Parameters

  • particles (int) – The number of particles in the particle swarm

  • iterations (int) – The number of particle swarm iterations

  • processes – The number of processes to use (the default None means use all available cpu cores)

Returns

the best parameters found

Return type

List of float

Note

On Windows, calling this function from a jupyter notebook can result in an error message of the form Can’t get attribute ‘apply_params’ on <module ‘__main__’, where apply_params is the function you have defined to apply the parameters to the model. This is a known issue with Python multiprocessing, and a workaround is to define the apply_params function in a separate .py file and import it into the notebook.