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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 from_structures(cls, structures, specie, temperature, time_step, step_skip, initial_disp=None, initial_structure=None, **kwargs):
 """ Convenient constructor that takes in a list of Structure objects to perform diffusion analysis. Args: structures ([Structure]):
 list of Structure objects (must be ordered in sequence of run). E.g., you may have performed sequential VASP runs to obtain sufficient statistics. specie (Element/Specie):
 Specie to calculate diffusivity for as a String. E.g., "Li". temperature (float):
 Temperature of the diffusion run in Kelvin. time_step (int):
 Time step between measurements. step_skip (int):
 Sampling frequency of the displacements ( time_step is multiplied by this number to get the real time between measurements) initial_disp (np.ndarray):
 Sometimes, you need to iteratively compute estimates of the diffusivity. This supplies an initial displacement that will be added on to the initial displacements. Note that this makes sense only when smoothed=False. initial_structure (Structure):
 Like initial_disp, this is used for iterative computations of estimates of the diffusivity. You typically need to supply both variables. This stipulates the initial structure from which the current set of displacements are computed. \\*\\*kwargs: kwargs supported by the :class:`DiffusionAnalyzer`_. Examples include smoothed, min_obs, avg_nsteps. """

        p, l = [], []
        for i, s in enumerate(structures):
            if i == 0:
                structure = s
            p.append(np.array(s.frac_coords)[:, None])
            l.append(s.lattice.matrix)
        if initial_structure is not None:
            p.insert(0, np.array(initial_structure.frac_coords)[:, None])
            l.insert(0, initial_structure.lattice.matrix)
        else:
            p.insert(0, p[0])
            l.insert(0, l[0])

        p = np.concatenate(p, axis=1)
        dp = p[:, 1:] - p[:, :-1]
        dp = dp - np.round(dp)
        f_disp = np.cumsum(dp, axis=1)
        c_disp = []
        for i in f_disp:
            c_disp.append( [ np.dot(d, m) for d, m in zip(i, l[1:]) ] )
        disp = np.array(c_disp)

        # If is NVT-AIMD, clear lattice data.
        if np.array_equal(l[0], l[-1]):
            l = np.array([l[0]])
        else:
            l = np.array(l)
        if initial_disp is not None:
            disp += initial_disp[:, None, :]

        return cls(structure, disp, specie, temperature, time_step,
                   step_skip=step_skip, lattices=l, **kwargs)