<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def least_squares_effective_mass( cartesian_k_points, eigenvalues ):
 """ Calculate the effective mass using a least squares quadratic fit. Args: cartesian_k_points (np.array):
 Cartesian reciprocal coordinates for the k-points eigenvalues (np.array):
 Energy eigenvalues at each k-point to be used in the fit. Returns: (float):
 The fitted effective mass Notes: If the k-points do not sit on a straight line a ValueError will be raised. """

    if not points_are_in_a_straight_line( cartesian_k_points ):
        raise ValueError( 'k-points are not collinear' )
    dk = cartesian_k_points - cartesian_k_points[0]
    mod_dk = np.linalg.norm( dk, axis = 1 )
    delta_e = eigenvalues - eigenvalues[0]
    effective_mass = 1.0 / ( np.polyfit( mod_dk, eigenvalues, 2 )[0] * ev_to_hartree * 2.0 )
    return effective_mass