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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 walk_regularity_symmetry(self, data_frame):
 """ This method extracts the step and stride regularity and also walk symmetry. :param data_frame: The data frame. It should have x, y, and z columns. :type data_frame: pandas.DataFrame :return step_regularity: Regularity of steps on [x, y, z] coordinates, defined as the consistency of the step-to-step pattern. :rtype step_regularity: numpy.ndarray :return stride_regularity: Regularity of stride on [x, y, z] coordinates, defined as the consistency of the stride-to-stride pattern. :rtype stride_regularity: numpy.ndarray :return walk_symmetry: Symmetry of walk on [x, y, z] coordinates, defined as the difference between step and stride regularity. :rtype walk_symmetry: numpy.ndarray """

        
        def _symmetry(v):
            maxtab, _ = peakdet(v, self.delta)
            return maxtab[1][1], maxtab[2][1]

        step_regularity_x, stride_regularity_x = _symmetry(autocorrelation(data_frame.x))
        step_regularity_y, stride_regularity_y = _symmetry(autocorrelation(data_frame.y))
        step_regularity_z, stride_regularity_z = _symmetry(autocorrelation(data_frame.z))

        symmetry_x = step_regularity_x - stride_regularity_x
        symmetry_y = step_regularity_y - stride_regularity_y
        symmetry_z = step_regularity_z - stride_regularity_z

        step_regularity = np.array([step_regularity_x, step_regularity_y, step_regularity_z])
        stride_regularity = np.array([stride_regularity_x, stride_regularity_y, stride_regularity_z])
        walk_symmetry = np.array([symmetry_x, symmetry_y, symmetry_z])

        return step_regularity, stride_regularity, walk_symmetry