def solve_eyring_rate_model_ss(voltage, _ss, _matrix, _test, _specs):
# 3 calculate the ion transport rates
solute_transport = eyring_script.eyring_rate_transport(_ss)
# characterize how well the steady state is by getting the residues of the steady state times the transition matrix
sum_squared_errors, sum_absolute_errors = characterize_solution(_ss, _matrix)
# 4 calculate the current from the solute transport rates
current = eyring_script.current_calc(solute_transport)
# save the fitting results in a custom class
fitting_specs_eig = FittingMetrics(_test, sum_absolute_errors, sum_squared_errors, solute_transport)
return Results(voltage, _specs, solute_transport, fitting_specs_eig,
current, _ss)
def solve_eyring_rate_model_ss(voltage, _ss, _matrix, _test, _specs):
# 3 calculate the ion transport rates
_solute_transport = eyring_script.eyring_rate_transport(_ss)
# transports are in 1x1 matrix form, so convert them to just scalars
solute_transport = dict()
for solute in _solute_transport:
solute_transport[solute] = [np.asscalar(x) for x in _solute_transport[solute]]
# characterize how well the steady state is by getting the residues of the steady state times the transition matrix
sum_squared_errors, sum_absolute_errors = characterize_solution(_ss, _matrix)
# 4 calculate the current from the solute transport rates
current = eyring_script.current_calc(solute_transport)
# save the fitting results in a custom class
fitting_specs_eig = FittingMetrics(_test, sum_absolute_errors, sum_squared_errors, solute_transport)
return Results(voltage, _specs, solute_transport, fitting_specs_eig,
current, _ss)
