How to use the mdtraj.load function in mdtraj

def test_atom_indices_1(get_fn):
    atom_indices = np.arange(10)
    top = md.load(get_fn('native.pdb'))
    t0 = md.load(get_fn('frame0.mdcrd'), top=top)
    t1 = md.load(get_fn('frame0.mdcrd'), top=top, atom_indices=atom_indices)

    eq(t0.xyz[:, atom_indices], t1.xyz)

def test_molecules(get_fn):
    top = md.load(get_fn('4OH9.pdb')).topology
    molecules = top.find_molecules()
    assert sum(len(mol) for mol in molecules) == top.n_atoms
    assert sum(1 for mol in molecules if len(mol) > 1) == 2  # All but two molecules are water

def test_trajectory_save_load(get_fn):
    t = md.load(get_fn('native.pdb'))
    t.unitcell_lengths = 1 * np.ones((1, 3))
    t.unitcell_angles = 90 * np.ones((1, 3))

    t.save(temp)
    t2 = md.load(temp, top=t.topology)

    eq(t.xyz, t2.xyz)
    eq(t.unitcell_lengths, t2.unitcell_lengths)

def _load_to_position(spec, arr_shape):
    '''
    Load a specified file into a specified position by spec. The
    arr_shape parameter lets us know how big the final array should be.
    '''
    (position, filename, load_kwargs) = spec

    xyz = md.load(filename, **load_kwargs).xyz

    # mp.Array must be converted to numpy array and reshaped
    arr = _tonumpyarray(shared_array).reshape(arr_shape)

    # dump coordinates in.
    arr[position:position+len(xyz)] = xyz

    return xyz.shape

'outfname' : 't4-tol',
            'nprop':5,
            'freeze_distance' : 10.0,
            #'write_ncmc' : 1,
            #'ncmc_traj': True 
            }

    #Generate the ParmEd Structure
    prmtop = utils.get_data_filename('blues', 'tests/data/eqToluene.prmtop')#
    inpcrd = utils.get_data_filename('blues', 'tests/data/eqToluene.inpcrd')
    struct = parmed.load_file(prmtop, xyz=inpcrd)
    struct = parmed.load_file(prmtop, xyz='posA.pdb')

    #Define the 'model' object we are perturbing here.
    # Calculate particle masses of object to be moved
    traj = md.load(inpcrd, top=prmtop)
    fit_atoms = traj.top.select("resid 50 to 155 and name CA")
    fit_atoms = traj.top.select("protein")
    ligand = MolDart(structure=struct, resname='LIG',
                                      pdb_files=['posB.pdb', 'posA.pdb'],
                                      #pdb_files=['posA.pdb', 'posB.pdb'],
                                      fit_atoms=fit_atoms)
#    ligand = RandomLigandRotationMove(struct, 'LIG')
#    ligand.calculateProperties()

    # Initialize object that proposes moves.
    ligand_mover = MoveEngine(ligand)

    # Generate the MD, NCMC, ALCHEMICAL Simulation objects
    simulations = SimulationFactory(struct, ligand_mover, **opt)
    simulations.createSimulationSet()

import pandas as pd
import nmrpystar
import mdtraj as md

stride = 100

t0 = md.load(["./Trajectories_ff99sbnmr/1am7_%d.dcd" % i for i in range(10)], top="./1am7_fixed.pdb")[::stride]
t1 = md.load(["./Trajectories/1am7_%d.dcd" % i for i in range(15)], top="./1am7_fixed.pdb")[::stride]


#full_prediction0 = md.nmr.chemical_shifts_shiftx2(t0)
#full_prediction1 = md.nmr.chemical_shifts_shiftx2(t1)

#full_prediction0 = md.nmr.chemical_shifts_spartaplus(t0)
#full_prediction1 = md.nmr.chemical_shifts_spartaplus(t1)

full_prediction0 = md.nmr.chemical_shifts_ppm(t0)
full_prediction1 = md.nmr.chemical_shifts_ppm(t1)



parsed = nmrpystar.parse(open("./16664.str").read())
print(parsed.status)

def __init__(self, args):
        self.args = args
        if args.top is not None:
            self.top = md.load(os.path.expanduser(args.top))
        else:
            self.top = None

        self.featurizer = mixtape.featurizer.load(args.featurizer)
        self.filenames = glob.glob(os.path.expanduser(args.dir) + '/*.' + args.ext)

Path to a trajectory containing timestep information to infer
        the correct timestep from when plotting implied timescales.
    """

    if timestep and infer_timestep:
        raise exception.ImproperlyConfigured(
            'Only one of --timestep and --infer-timestep can be '
            'supplied, you supplied both --timestep=%s and '
            '--infer-timestep=%s' % (timestep, infer_timestep))

    if timestep:
        unit_factor = timestep
        unit_str = 'ns'
    elif infer_timestep:
        try:
            timestep = md.load(infer_timestep).timestep
        except ValueError:
            if infer_timestep[-4:] != '.xtc':
                raise exception.ImproperlyConfigured(
                    "Topologyless formats other than XTC are not supported.")
            with md.formats.xtc.XTCTrajectoryFile(infer_timestep) as f:
                xyz, time, step, box = f.read(n_frames=10)
                timesteps = time[1:] - time[0:-1]
                assert np.all(timesteps[0] == timesteps)
                timestep = timesteps[0]
        unit_factor = 1000 / timestep  # units are ps
        unit_str = 'ns'
    else:
        unit_factor = 1
        unit_str = 'frames'

    return unit_factor, unit_str

def single_traj_from_n_files(file_list, top):
    """ Creates a single trajectory object from a list of files

    """
    traj = None
    for ff in file_list:
        if traj is None:
            traj = md.load(ff, top=top)
        else:
            traj = traj.join(md.load(ff, top=top))

    return traj