I cant figure out how to send an array out of the optimization.minimize function

Question

I am building a Python application for use in sientific computing. The application is a Model Predicive Controler (MPC) and I am using scipy.optimize.minimize function as the optimization algorithm.

solution_guess = minimize(objectiveFunction,
                                  U_guess,
                                  arg_guess,
                                  callback= None,
                                  method = "SLSQP")

where the objective function is a self made function where a simulation of my system is executed. it looks some like this:

def objectiveFunction(x,*arg):
    U_test = x
    dt_test = arg[0]
    setpoint_test = arg[1]
    pred_horizion_length_test = arg[2]
    initStateValue_test = arg[3]
    # Defining Model Arrays
    NS_pred_horizion_test = int(pred_horizion_length_test/dt_test)+1
    pred_horizion_array_test = np.linspace(0, pred_horizion_length_test, NS_pred_horizion_test)
    SP_array_test = np.zeros(NS_pred_horizion_test) + setpoint_test
    Y_array_test = SP_array_test * 0

    # Defining parameters for the testing model
    timeDelay_test = 50
    initDelayValue_test = 0
    K_test = 4
    Tc1_test = 30
    Tc2_test = 60

    # Defining Model Object
    obj_model_test = model.secDegModel(dt = dt_test,
                                      K = K_test,
                                      Tc1 = Tc1_test,
                                      Tc2 = Tc2_test,
                                      timeDelay = timeDelay_test,
                                      initStateValue = initStateValue_test,
                                      initDelayValue = initDelayValue_test
                                      )


    ###########################################
    #|||||||||||||||||||||||||||||||||||||||||#
    #     Testing Values for U on Model       #
    #|||||||||||||||||||||||||||||||||||||||||#
    ###########################################


    # Running simulation of "real" model function
    for k in range(NS_pred_horizion_test):
        Y_array_test[k] = obj_model_test.run(u_k = U_test) 

    error = np.sum(abs(SP_array_test-Y_array_test))
    return error

What i struggle with no is how to get back the Y_array_test array so I can plot it every time the optimization is done. I tryed using global variables but i did not get it to work nor do I think its good coding manner to use global variables. does any one know a nice way to solv my problem? maybe using callback function? (if callback is the way to go, I do not fully understand how this method works or how to implement it in a nice way)

Answer 1

Why don't you just do the following?

Modify your objectiveFunction as follows

from numpy import savez

def objectiveFunction(x,*arg):
.
.
.
.
.

# Running simulation of "real" model function
for k in range(NS_pred_horizion_test):
    Y_array_test[k] = obj_model_test.run(u_k = U_test) 
# Just save Y_array_test in a file
# Add some call_no if you don't want to overwrite
# then filename would be 'Y_array_test_' + str(call_no) 
# You could increment this call_no, every time by 
# call_no = call_no + 1
savez(file_name, Y_array_test)
# Then you could plot it outside using matplotlib
error = np.sum(abs(SP_array_test-Y_array_test))
return error