def METHOD_NAME(self):
    stripfun = bgtheories.THEORY["Strip"].function
    anchors = sorted(random.sample(list(self.x), 4))
    anchors_indices = [list(self.x).index(a) for a in anchors]
    # we really want to strip away the narrow peak
    anchors_indices_copy = copy.deepcopy(anchors_indices)
    for idx in anchors_indices_copy:
        if abs(idx - self.narrow_peak_index) < 5:
            anchors_indices.remove(idx)
            anchors.remove(self.x[idx])
    width = 2
    niter = 1000
    bgtheories.THEORY["Strip"].configure(AnchorsList=anchors, AnchorsFlag=True)
    bg = stripfun(self.x, self.y, width, niter)
    # assert peak amplitude has been decreased
    self.assertLess(bg[self.narrow_peak_index],
                    self.y[self.narrow_peak_index])
    # default estimate
    for i in anchors_indices:
        self.assertEqual(bg[i], self.y[i])
    # estimated parameters are equal to the default ones in the config dict
    bgtheories.THEORY["Strip"].configure(StripWidth=7, StripIterations=8)
    esti_par, cons = bgtheories.THEORY["Strip"].estimate(self.x, self.y)
    self.assertTrue(numpy.array_equal(cons, [[3, 0, 0], [3, 0, 0]]))
    self.assertEqual(esti_par, [7, 8])