How to use the mdtraj.compute_dihedrals function in mdtraj

platformNames = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]

        if 'CUDA' in platformNames:
            runNCMC('CUDA', relaxstepsNC, mdstep)
        else:
            if options.force:
                runNCMC('CPU', relaxstepsNC, mdstep)
            else:
                print('WARNING: Could not find a valid CUDA/OpenCL platform. BLUES is not recommended on CPUs.')
                print("To run on CPU: 'python blues/example.py -f'")

        di_dataFN = "dihedrals%iNC_gp%i_MD1000step.txt" %(relaxstepsNC, repeat)
        traj = md.load_dcd('accept.dcd', top = 'protein.pdb')
        indicies = np.array([[0, 4, 6, 8]])
        dihedraldata = md.compute_dihedrals(traj, indicies)
        datafile = open(di_dataFN,'w')
        for value in dihedraldata:
            datafile.write("%s\n" % str(value)[1:-1])

        datafile.close()

platformNames = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())]

        if 'CUDA' in platformNames:
            runNCMC('CUDA', relaxstepsNC, mdstep)
        else:
            if options.force:
                runNCMC('CPU', relaxstepsNC, mdstep)
            else:
                print('WARNING: Could not find a valid CUDA/OpenCL platform. BLUES is not recommended on CPUs.')
                print("To run on CPU: 'python blues/example.py -f'")

        di_dataFN = "dihedrals%iNC_gp%i_MD1000step.txt" %(relaxstepsNC, repeat)
        traj = md.load_dcd('accept.dcd', top = 'protein.pdb')
        indicies = np.array([[0, 4, 6, 8]])
        dihedraldata = md.compute_dihedrals(traj, indicies)
        datafile = open(di_dataFN,'w')
        for value in dihedraldata:
            datafile.write("%s\n" % str(value)[1:-1])

        datafile.close()

box_vectors=init_box,
                                       velocities=init_vel)

    engine.equilibrate(5)
    snap = engine.current_snapshot
    engine.storage.snapshots.save()
    engine.initialized = True
    SampleGeneratingMover.engine = engine

    # this generates an order parameter (callable) object named psi (so if
    # we call `psi(trajectory)` we get a list of the values of psi for each
    # frame in the trajectory). This particular order parameter uses
    # mdtraj's compute_dihedrals function, with the atoms in psi_atoms

    psi_atoms = [6,8,14,16]
    psi = CV_Function("psi", md.compute_dihedrals, trajdatafmt="mdtraj",
                      indices=[psi_atoms])

    # same story for phi, although we won't use that

    phi_atoms = [4,6,8,14]
    phi = CV_Function("phi", md.compute_dihedrals, trajdatafmt="mdtraj",
                      indices=[phi_atoms])

    # save the collectivevariables in the storage
    # since they have no data cache this will only contain their name
    psi.save(storage=engine.storage.collectivevariables)
    phi.save(storage=engine.storage.collectivevariables)

    # now we define our states and our interfaces
    degrees = 180/3.14159 # psi reports in radians; I think in degrees
    stateA = CVRangeVolumePeriodic(psi, -120.0/degrees, -30.0/degrees)

def getDihedral(self, positions, atomlist):
        """This function computes the dihedral angle for the given atoms at the given positions"""
        top = self.structure.topology
        traj = mdtraj.Trajectory(numpy.asarray(positions),top)
        angle = mdtraj.compute_dihedrals(traj, atomlist)
        return angle

def getDihedral(self, positions, atomlist):
        """This function computes the dihedral angle for the given atoms at the given positions"""
        top = self.structure.topology
        traj = mdtraj.Trajectory(np.asarray(positions),top)
        angle = mdtraj.compute_dihedrals(traj, atomlist)
        return angle

def load_data(self):
        load_time_start = time.time()
        data = []
        for tfn in self.filenames:
            kwargs = {} if tfn.endswith('h5') else {'top': self.top}
            for t in md.iterload(tfn, chunk=self.args.split, **kwargs):
                item = np.asarray(md.compute_dihedrals(t, self.indices), np.double)
                data.append(item)

        print('Loading data into memory + vectorization: %f s' %
              (time.time() - load_time_start))
        print('''Fitting with %s timeseries from %d trajectories with %d
                total observations''' % (len(data), len(self.filenames),
                                         sum(len(e) for e in data)))
        return data

engine.initialized = True
    SampleGeneratingMover.engine = engine

    # this generates an order parameter (callable) object named psi (so if
    # we call `psi(trajectory)` we get a list of the values of psi for each
    # frame in the trajectory). This particular order parameter uses
    # mdtraj's compute_dihedrals function, with the atoms in psi_atoms

    psi_atoms = [6,8,14,16]
    psi = CV_Function("psi", md.compute_dihedrals, trajdatafmt="mdtraj",
                      indices=[psi_atoms])

    # same story for phi, although we won't use that

    phi_atoms = [4,6,8,14]
    phi = CV_Function("phi", md.compute_dihedrals, trajdatafmt="mdtraj",
                      indices=[phi_atoms])

    # save the collectivevariables in the storage
    # since they have no data cache this will only contain their name
    psi.save(storage=engine.storage.collectivevariables)
    phi.save(storage=engine.storage.collectivevariables)

    # now we define our states and our interfaces
    degrees = 180/3.14159 # psi reports in radians; I think in degrees
    stateA = CVRangeVolumePeriodic(psi, -120.0/degrees, -30.0/degrees)
    stateB = CVRangeVolumePeriodic(psi, 100/degrees, 180/degrees)

    # set up minima and maxima for this transition's interface set
    minima = map((1.0 / degrees).__mul__,
                 [-125, -135, -140, -142.5, -145.0, -147.0, 150.0])
    maxima = map((1.0 / degrees).__mul__,

for index, entry in enumerate(mapped_data[0]):
    frame_dict[entry].append(index)
for num in range(num_clusters):
    print len(frame_dict[num])
for num in range(num_clusters):
    output_name = 'tcluster_' + str(num) + '.dcd'
    print 'saving ', output_name
    output_traj(main_file, outname=output_name, framelist=frame_dict[num])
print mapped_data
traj = md.load(main_file)
if 1: 
    angle_index = np.array([[1635, 1639, 2640]]) #makes symmetric groups
    angle_output = md.compute_angles(traj, angle_index)
    dihedral_index = np.array([[1638, 2634, 2636, 2640]])  #separates into four groups
    dihedral_output = md.compute_dihedrals(traj, dihedral_index)
    frames = traj.n_frames

    f, (ax1, ax2) = plt.subplots(2)
    ax1.scatter(range(frames), angle_output, marker='x', s=2)
    print angle_output
    print dihedral_output
    ax2.hist2d(angle_output[:,0], dihedral_output[:,0], bins=[40,40])
    plt.show()

frames = traj.n_frames
print frames
f, (ax1, ax2) = plt.subplots(2)
ax1.scatter(range(frames), angle_output, marker='x', s=2)
ax2.hist(angle_output, bins=180)

#plt.scatter(range(frames), angle_output)