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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def gauss_fit(X, Y):
 """ Fit the function to a gaussian. Parameters X: 1d array X values Y: 1d array Y values Returns ------- (The return from scipy.optimize.curve_fit) popt : array Optimal values for the parameters pcov : 2d array The estimated covariance of popt. Notes ----- /!\ This uses a slow curve_fit function! do not use if need speed! """

    X = np.asarray(X)
    Y = np.asarray(Y)
    # Can not have negative values
    Y[Y < 0] = 0
    # define gauss function

    def gauss(x, a, x0, sigma):
        return a * np.exp(-(x - x0)**2 / (2 * sigma**2))
    # get first estimation for parameter
    mean = (X * Y).sum() / Y.sum()
    sigma = np.sqrt((Y * ((X - mean)**2)).sum() / Y.sum())
    height = Y.max()
    # fit with curve_fit
    return curve_fit(gauss, X, Y, p0=[height, mean, sigma])