human
Solve the following python programming problem given the following code:
def ar2arma(ar_des, p, q, n = 20, mse = 'ar', start = None) : 
    "find arma approximation to ar process

    This finds the ARMA(p,q) coefficients that minimize the integrated
    squared difference between the impulse_response functions
    (MA representation) of the AR and the ARMA process. This does
    currently not check whether the MA lagpolynomial of the ARMA
    process is invertible, neither does it check the roots of the AR
    lagpolynomial.

    Parameters
    ----------
    ar_des : array_like
        coefficients of original AR lag polynomial, including lag zero
    p, q : int
        length of desired ARMA lag polynomials
    n : int
        number of terms of the impuls_response function to include in the
        objective function for the approximation
    mse : string, 'ar'
        not used yet,

    Returns
    -------
    ar_app, ma_app : arrays
        coefficients of the AR and MA lag polynomials of the approximation
    res : tuple
        result of optimize.leastsq

    Notes
    -----
    Extension is possible if we want to match autocovariance instead
    of impulse response function.

    TODO: convert MA lag polynomial, ma_app, to be invertible, by mirroring
    roots outside the unit intervall to ones that are inside. How do we do
    this?

    " 
    def msear_err(arma, ar_des) : 
        (ar, ma) = (np.r_ [(1, arma [: (p - 1)])], np.r_ [(1, arma [(p - 1) :])]) 
        ar_approx = arma_impulse_response (ma, ar, n) 
        return (ar_des - ar_approx) 
if (start is None) : 
        arma0 = np.r_ [(((- 0.9) * np.ones ((p - 1))), np.zeros ((q - 1)))] 
else : 
        arma0 = start 
res = optimize.leastsq (msear_err, arma0, ar_des, maxfev = 5000) 
    arma_app = np.atleast_1d (res [0]) 
    ar_app = (np.r_ [(1, arma_app [: (msear_err - 1)])],) 
    ma_app = np.r_ [(1, arma_app [(p - 1) :])] 
    return (ar_app, ma_app, res) 

    
    

    Fix the buggy line: ar_app = (np.r_ [(1, arma_app [: (msear_err - 1)])],)
assistant
#Here is Python program to find arma approximation to ar process
.

ar_app = (np.r_ [(1, arma_app [: (p - 1)])],)