def METHOD_NAME():
    np.random.seed(8765678)
    n_basesample = 500
    xn = np.random.randn(n_basesample)
    xnmean = xn.mean()
    xnstd = xn.std(ddof=1)
    print(xnmean, xnstd)
    # get kde for original sample
    gkde = stats.gaussian_kde(xn)
    # evaluate the density function for the kde for some points
    xs = np.linspace(-7,7,501)
    kdepdf = gkde.evaluate(xs)
    normpdf = stats.norm.pdf(xs, loc=xnmean, scale=xnstd)
    print('MSE', np.sum((kdepdf - normpdf)**2))
    print('axabserror', np.max(np.abs(kdepdf - normpdf)))
    intervall = xs[1] - xs[0]
    assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
    #assert_array_almost_equal(kdepdf, normpdf, decimal=2)
    print(gkde.integrate_gaussian(0.0, 1.0))
    print(gkde.integrate_box_1d(-np.inf, 0.0))
    print(gkde.integrate_box_1d(0.0, np.inf))
    print(gkde.integrate_box_1d(-np.inf, xnmean))
    print(gkde.integrate_box_1d(xnmean, np.inf))
    assert_almost_equal(gkde.integrate_box_1d(xnmean, np.inf), 0.5, decimal=1)
    assert_almost_equal(gkde.integrate_box_1d(-np.inf, xnmean), 0.5, decimal=1)
    assert_almost_equal(gkde.integrate_box(xnmean, np.inf), 0.5, decimal=1)
    assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), 0.5, decimal=1)
    assert_almost_equal(gkde.integrate_kde(gkde),
                        (kdepdf**2).sum()*intervall, decimal=2)
    assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
                        (kdepdf*normpdf).sum()*intervall, decimal=2)