Esempio n. 1
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def convert_mdtraj_to_oemol(traj: md.Trajectory) -> oechem.OEMol:
    """
    This method converts an mdtraj Trajectory to an OEMol via saving as a PDBfile
    and reading in with OpenEye. Although this seems hacky, it seems less error-prone
    than trying to manually construct the OEMol.

    Parameters
    ----------
    mdtraj: md.Trajectory
        The trajectory to turn into an OEMol
    
    Returns
    -------
    mol : oechem.OEMol
        The trajectory represented as an OEMol
    """
    #create a temporary file with a PDB suffix and save with MDTraj
    pdb_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdb")
    traj.save(pdb_file.name)
    pdb_file.close()
    
    #Now use the openeye oemolistream to read in this file as an OEMol:
    ifs = oechem.oemolistream()
    ifs.open(pdb_file.name)
    ifs.SetFormat(oechem.OEFormat_PDB)
    
    mol = oechem.OEMol()
    oechem.OEReadMolecule(ifs, mol)
    
    #close the stream and delete the temporary pdb file
    ifs.close()
    os.unlink(pdb_file.name)

    return mol
Esempio n. 2
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def write_equilibrium_trajectory(equilibrium_result: EquilibriumResult,
                                 trajectory: md.Trajectory,
                                 trajectory_filename: str) -> float:
    """
    Write the results of an equilibrium simulation to disk. This task will append the results to the given filename.
    Parameters
    ----------
    equilibrium_result : EquilibriumResult namedtuple
        the result of an equilibrium calculation
    trajectory : md.Trajectory
        the trajectory resulting from an equilibrium simulation
    trajectory_filename : str
        the name of the trajectory file to which we should append

    Returns
    -------
    reduced_potential_final_frame : float
        the reduced potential of the final frame
    """
    if not os.path.exists(trajectory_filename):
        trajectory.save_hdf5(trajectory_filename)
    else:
        written_traj = md.load_hdf5(trajectory_filename)
        concatenated_traj = written_traj.join(trajectory)
        concatenated_traj.save_hdf5(trajectory_filename)

    return equilibrium_result.reduced_potential
Esempio n. 3
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def mdtraj_to_oemol(snapshot: md.Trajectory):
    """
    Create an OEMol from an MDTraj file by writing and reading

    NOTE: This uses terrible heuristics

    Parameters
    ----------
    snapshot : mdtraj.Trajectory
        MDTraj Trajectory with a single snapshot

    Returns
    -------
    oemol : openeye.oechem.OEMol
        The OEMol

    """
    from openeye import oechem

    with tempfile.TemporaryDirectory() as tmpdir:
        filename = os.path.join(tmpdir, "tmp.pdb")
        # Write the PDB file
        snapshot.save(filename)
        # Read it with OpenEye
        with oechem.oemolistream(filename) as ifs:
            for mol in ifs.GetOEGraphMols():
                return mol
Esempio n. 4
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def write_nonequilibrium_trajectory(
        nonequilibrium_result: NonequilibriumResult,
        nonequilibrium_trajectory: md.Trajectory,
        trajectory_filename: str) -> float:
    """
    Write the results of a nonequilibrium switching trajectory to a file. The trajectory is written to an
    mdtraj hdf5 file, whereas the cumulative work is written to a numpy file.

    Parameters
    ----------
    nonequilibrium_result : NonequilibriumResult namedtuple
        The result of a nonequilibrium switching calculation
    nonequilibrium_trajectory : md.Trajectory
        The trajectory resulting from a nonequilibrium simulation
    trajectory_filename : str
        The full filepath for where to store the trajectory

    Returns
    -------
    final_work : float
        The final value of the work trajectory
    """
    if nonequilibrium_trajectory is not None:
        nonequilibrium_trajectory.save_hdf5(trajectory_filename)

    return nonequilibrium_result.cumulative_work[-1]
Esempio n. 5
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def convert_mdtraj_to_oemol(traj: md.Trajectory) -> oechem.OEMol:
    """
    This method converts an mdtraj Trajectory to an OEMol via saving as a PDBfile
    and reading in with OpenEye. Although this seems hacky, it seems less error-prone
    than trying to manually construct the OEMol.

    Parameters
    ----------
    mdtraj: md.Trajectory
        The trajectory to turn into an OEMol

    Returns
    -------
    mol : oechem.OEMol
        The trajectory represented as an OEMol
    """
    # create a temporary file with a PDB suffix and save with MDTraj
    pdb_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdb")
    traj.save(pdb_file.name)
    pdb_file.close()

    # Now use the openeye oemolistream to read in this file as an OEMol:
    ifs = oechem.oemolistream()
    ifs.open(pdb_file.name)
    ifs.SetFormat(oechem.OEFormat_PDB)

    mol = oechem.OEMol()
    oechem.OEReadMolecule(ifs, mol)

    # close the stream and delete the temporary pdb file
    ifs.close()
    os.unlink(pdb_file.name)

    return mol
Esempio n. 6
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def write_equilibrium_trajectory(equilibrium_result: EquilibriumResult, trajectory: md.Trajectory, trajectory_filename: str) -> float:
    """
    Write the results of an equilibrium simulation to disk. This task will append the results to the given filename.
    Parameters
    ----------
    equilibrium_result : EquilibriumResult namedtuple
        the result of an equilibrium calculation
    trajectory : md.Trajectory
        the trajectory resulting from an equilibrium simulation
    trajectory_filename : str
        the name of the trajectory file to which we should append

    Returns
    -------
    reduced_potential_final_frame : float
        the reduced potential of the final frame
    """
    if not os.path.exists(trajectory_filename):
        trajectory.save_hdf5(trajectory_filename)
    else:
        written_traj = md.load_hdf5(trajectory_filename)
        concatenated_traj = written_traj.join(trajectory)
        concatenated_traj.save_hdf5(trajectory_filename)

    return equilibrium_result.reduced_potential
Esempio n. 7
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def write_equilibrium_trajectory(trajectory: md.Trajectory,
                                 trajectory_filename: str) -> float:
    """
    Write the results of an equilibrium simulation to disk. This task will append the results to the given filename.

    Arguments
    ----------
    trajectory : md.Trajectory
        the trajectory resulting from an equilibrium simulation
    trajectory_filename : str
        the name of the trajectory file to which we should append

    Returns
    -------
    True
    """
    if not os.path.exists(trajectory_filename):
        trajectory.save_hdf5(trajectory_filename)
        _logger.debug(
            f"{trajectory_filename} does not exist; instantiating and writing to."
        )
    else:
        _logger.debug(f"{trajectory_filename} exists; appending.")
        written_traj = md.load_hdf5(trajectory_filename)
        concatenated_traj = written_traj.join(trajectory)
        concatenated_traj.save_hdf5(trajectory_filename)

    return True
Esempio n. 8
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def test_lprmsd_0():
    # remap a permutation of all the atoms with no rotation
    ref = random.randn(1, 10, 3).astype(np.float32)
    mapping = random.permutation(10)
    print('true mapping', mapping)
    new = ref[:, mapping]

    value = lprmsd(Trajectory(xyz=new, topology=None), Trajectory(xyz=ref, topology=None))
    eq(value, np.array([0.0], dtype=np.float32), decimal=3)
Esempio n. 9
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def test_lprmsd_1():
    # resolve a random rotation with no permutation
    ref = random.randn(1, 50, 3).astype(np.float32)
    mapping = np.arange(50)
    rot = rotation_matrix_from_quaternion(uniform_quaternion())
    new = ref[:, mapping].dot(rot)

    value = lprmsd(Trajectory(xyz=new, topology=None), Trajectory(xyz=ref, topology=None), permute_groups=[[]])
    assert value[0] < 1e-2
Esempio n. 10
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def test_lprmsd_2():
    # resolve a random rotation with some permutation
    ref = random.randn(1, 50, 3).astype(np.float32)
    # first half of the atoms can permute, last 10 are fixed permutation
    mapping = np.concatenate((random.permutation(10), 10 + np.arange(40)))
    rot = rotation_matrix_from_quaternion(uniform_quaternion())
    new = ref[:, mapping].dot(rot)
    
    value = lprmsd(Trajectory(xyz=new, topology=None), Trajectory(xyz=ref, topology=None), permute_groups=[np.arange(10)])
    assert value[0] < 1e-2
Esempio n. 11
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def test_unitcell():
    # make sure that bogus unitcell vecotrs are not saved
    top = md.load(get_fn('native.pdb')).restrict_atoms(range(5)).topology
    t = Trajectory(xyz=np.random.randn(100, 5, 3), topology=top)

    #           xtc    dcd   binpos  trr    h5     pdb    nc     lh5
    for fn in [temp1, temp2, temp3, temp4, temp5, temp6, temp6, temp8]:
        t.save(fn)
        f = lambda: eq(md.load(fn, top=top).unitcell_vectors, None)
        f.description = 'unitcell preservation in %s' % os.path.splitext(fn)[1]
        yield f
Esempio n. 12
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def test_unitcell():
    # make sure that bogus unitcell vecotrs are not saved
    top = md.load(get_fn('native.pdb')).restrict_atoms(range(5)).topology
    t = Trajectory(xyz=np.random.randn(100, 5, 3), topology=top)

    #           xtc    dcd   binpos  trr    h5     pdb    nc     lh5
    for fn in [temp1, temp2, temp3, temp4, temp5, temp6, temp6, temp8]:
        t.save(fn)
        f = lambda: eq(md.load(fn, top=top).unitcell_vectors, None)
        f.description = 'unitcell preservation in %s' % os.path.splitext(fn)[1]
        yield f
Esempio n. 13
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    def setup(self):

        self.q_values  = np.array([1.0, 2.0])
        self.num_phi   = 360
        self.traj      = Trajectory.load(ref_file('ala2.pdb'))
        self.num_shots = 2

        # generate the tables file on disk, then re-open it
        intensities = np.abs( np.random.randn(self.num_shots, len(self.q_values),
                                              self.num_phi) / 100.0 + \
                              np.cos( np.linspace(0.0, 4.0*np.pi, self.num_phi) ) )

        if os.path.exists('tmp_tables.h5'):
            os.remove('tmp_tables.h5')
            
        hdf = tables.File('tmp_tables.h5', 'w')
        a = tables.Atom.from_dtype(np.dtype(np.float64))
        node = hdf.create_earray(where='/', name='data',
                                shape=(0, len(self.q_values), self.num_phi), 
                                atom=a, filters=io.COMPRESSION)
        node.append(intensities)
        hdf.close()

        self.tables_file = tables.File('tmp_tables.h5', 'r+')
        pi = self.tables_file.root.data
        pm = np.random.binomial(1, 0.9, size=(len(self.q_values), self.num_phi))
        k = 1.0
        
        self.rings = xray.Rings(self.q_values, pi, k, pm)

        return
Esempio n. 14
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    def test_select_positions(self, universe: md.Trajectory,
                              n_frames: int) -> None:
        """Test get_positions function using atom selection.

        GIVEN topology and trajectory filenames and an atom selection
        WHEN the get_positions function is called
        THEN return a array of positions with shape (n_frames, n_atoms, 3)

        Parameters
        ----------
        universe : Trajectory
            Molecular dynamics trajectory
        n_frames : int
            Number of frames
        """
        mask = "protein and name CA"
        atoms = universe.topology.select(mask)
        n_atoms = atoms.size

        array = utils.get_positions(
            TOPWW,
            [
                TRJWW,
            ],
            mask=mask,
        )
        assert array.shape == (n_frames, n_atoms, 3)
        testing.assert_allclose(array[0],
                                universe.atom_slice(atoms).xyz[0] * 10)
        assert isinstance(array, np.ndarray)
def traj_frame_to_sampler_state(traj: md.Trajectory, frame_number: int,
                                box_vectors):
    xyz = traj.xyz[frame_number, :, :]
    box_vectors = traj.openmm_boxes(frame_number)
    sampler_state = states.SamplerState(
        unit.Quantity(xyz, unit=unit.nanometers))
    return sampler_state
Esempio n. 16
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    def test_select_average(self, universe: md.Trajectory) -> None:
        """Test get_average_structure function using atom selection.

        GIVEN topology and trajectory filenames and an atom selection
        WHEN the get_average_structure function is called
        THEN the average coordinates are computed

        Parameters
        ----------
        universe : Trajectory
            Molecular dynamics trajectory
        """
        mask = "protein and name CA"
        atoms = universe.topology.select(mask)
        n_atoms = atoms.size

        average = utils.get_average_structure(
            TOPWW,
            [
                TRJWW,
            ],
            mask=mask,
        )
        assert average.xyz.shape == (1, n_atoms, 3)
        universe_average = universe.atom_slice(atoms).xyz.mean(axis=0)
        testing.assert_allclose(average.xyz[0], universe_average)
Esempio n. 17
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 def setup(self):
     self.q_values  = np.array([1.0, 2.0])
     self.num_phi   = 360
     self.traj      = Trajectory.load(ref_file('ala2.pdb'))
     self.num_shots = 4
     self.rings     = xray.Rings.simulate(self.traj, 1, self.q_values,
                                          self.num_phi, self.num_shots) # 1 molec
Esempio n. 18
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    def read_as_traj(self, iteration=None, segment=None, atom_indices=None):
        _check_mode(self.mode, ('r', ))

        pnode = self._get_node(where='/', name='pointer')

        iter_labels = pnode[:, 0]
        seg_labels = pnode[:, 1]

        if iteration is None and segment is None:
            frame_indices = slice(None)
        elif isinstance(iteration, (np.integer, int)) and isinstance(
                segment, (np.integer, int)):
            frame_torf = np.logical_and(iter_labels == iteration,
                                        seg_labels == segment)
            frame_indices = np.arange(len(iter_labels))[frame_torf]
        else:
            raise ValueError(
                "iteration and segment must be integers and provided at the same time"
            )

        if len(frame_indices) == 0:
            raise ValueError(
                f"no frame was selected: iteration={iteration}, segment={segment}, atom_indices={atom_indices}"
            )

        iter_labels = iter_labels[frame_indices]
        seg_labels = seg_labels[frame_indices]

        topology = self.topology
        if atom_indices is not None:
            topology = topology.subset(atom_indices)

        data = self.read(frame_indices=frame_indices,
                         atom_indices=atom_indices)
        if len(data) == 0:
            return Trajectory(xyz=np.zeros((0, topology.n_atoms, 3)),
                              topology=topology)

        in_units_of(data.coordinates,
                    self.distance_unit,
                    Trajectory._distance_unit,
                    inplace=True)
        in_units_of(data.cell_lengths,
                    self.distance_unit,
                    Trajectory._distance_unit,
                    inplace=True)

        return WESTTrajectory(
            data.coordinates,
            topology=topology,
            time=data.time,
            unitcell_lengths=data.cell_lengths,
            unitcell_angles=data.cell_angles,
            iter_labels=iter_labels,
            seg_labels=seg_labels,
            pcoords=None,
        )
Esempio n. 19
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 def setup(self):
     self.q_values = np.array([1.0, 2.0])
     self.num_phi  = 360
     self.l = 50.0
     self.d = xray.Detector.generic(spacing=0.4, l=self.l)
     self.num_shots = 2
     self.i = np.abs( np.random.randn(self.num_shots, self.d.num_pixels) )
     self.t = Trajectory.load(ref_file('ala2.pdb'))
     self.shot = xray.Shotset(self.i, self.d)
Esempio n. 20
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def _join_traj_data(traj_data, top_file):
    top = load_topology_cached(top_file)
    xyz = np.concatenate(tuple(map(itemgetter(0), traj_data)))

    traj = Trajectory(xyz, top)

    if all(t.unitcell_lengths is not None for t in traj_data):
        unitcell_lengths = np.concatenate(tuple(map(itemgetter(1), traj_data)))
        traj.unitcell_lengths = unitcell_lengths

    if all(t.box is not None for t in traj_data):
        boxes = np.concatenate(tuple(map(itemgetter(-1), traj_data)))
        traj.unitcell_vectors = boxes

    if all(t.unitcell_angles is not None for t in traj_data):
        angles = np.concatenate(tuple(map(itemgetter(2), traj_data)))
        traj.unitcell_angles = angles

    return traj
Esempio n. 21
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def load_lh5(filename, top=None, stride=None, atom_indices=None, frame=None):
    """Load an deprecated MSMBuilder2 LH5 trajectory file.

    Parameters
    ----------
    filename : str
        filename of AMBER NetCDF file.
    top : {str, Trajectory, Topology}
        The NetCDF format does not contain topology information. Pass in either
        the path to a pdb file, a trajectory, or a topology to supply this
        information.
    stride : int, default=None
        Only read every stride-th frame
    atom_indices : array_like, optional
        If not none, then read only a subset of the atoms coordinates from the
        file. This may be slightly slower than the standard read because it
        requires an extra copy, but will save memory.
    frame : int, optional
        Use this option to load only a single frame from a trajectory on disk.
        If frame is None, the default, the entire trajectory will be loaded.
        If supplied, ``stride`` will be ignored.

    See Also
    --------
    mdtraj.LH5TrajectoryFile :  Low level interface to LH5 files
    """
    from mdtraj import Trajectory

    atom_indices = cast_indices(atom_indices)
    with LH5TrajectoryFile(filename) as f:
        if frame is not None:
            f.seek(frame)
            xyz = f.read(n_frames=1, atom_indices=atom_indices)
        else:
            xyz = f.read(stride=stride, atom_indices=atom_indices)

        topology = f.topology
        in_units_of(xyz,
                    f.distance_unit,
                    Trajectory._distance_unit,
                    inplace=True)

        if atom_indices is not None:
            topology = f.topology.subset(atom_indices)

    time = np.arange(len(xyz))
    if frame is not None:
        time += frame
    elif stride is not None:
        time *= stride

    return Trajectory(xyz=xyz, topology=topology, time=time)
Esempio n. 22
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    def _construct_traj(self):
        """
        Create an mdtraj.Trajectory from the CG topology and xyz.

        """

        cg_traj = Trajectory(self._cg_xyz,
                             self._cg_top,
                             time=self._aa_traj.time,
                             unitcell_lengths=self._aa_traj.unitcell_lengths,
                             unitcell_angles=self._aa_traj.unitcell_angles)

        self._cg_traj = cg_traj
Esempio n. 23
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def test_sph_harm():
    
    # -----------------------
    traj = Trajectory.load(ref_file('pentagon.pdb'))
    
    q_magnitudes     = [1.6]
    num_coefficients = 44

    num_molecules = 1
    num_shots     = 20000
    num_phi       = 2048
    # -----------------------
        
    q = q_magnitudes[0]


    # compute the Kam-theoretic values of the Legendre coefficients C_ell, which
    # we will call "coeffsh"
    coeffsh_even = scatter.sph_harm_coefficients(traj, q_magnitudes,
                                                 num_coefficients=num_coefficients/2)
    coeffsh_even = np.nan_to_num(coeffsh_even)
    coeffsh_even /= coeffsh_even[1]

    coeffsh = np.zeros(num_coefficients)
    coeffsh[0::2] = coeffsh_even.flatten()


    # next, preform a simulation of the scattering and empirically compute the
    # correlation function
    rings = xray.Rings.simulate(traj, num_molecules, q_magnitudes, 
                                num_phi, num_shots)
    
    c = rings.correlate_intra(q, q, mean_only=True)

    # it seems best to compare the solutions in the expanded basis
    c_sh = np.polynomial.legendre.legval(rings.cospsi(q, q), coeffsh.flatten())
    
    c    = c - c.mean()
    c_sh = c_sh - c_sh.mean()
    
    # plt.figure()
    # plt.plot(c_sh / c_sh[0])
    # plt.plot(c / c[0])
    # plt.show()
    
    # if these are more than 10% different, fail the test
    error = (np.sum(np.abs( (c_sh / c_sh[0]) - (c / c[0]) )) / float(num_phi))
    assert error < 0.1, 'simulation and analytical computation >10%% different (%f %%)' % error
    
    return
Esempio n. 24
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def write_nonequilibrium_trajectory(nonequilibrium_result: NonequilibriumResult, nonequilibrium_trajectory: md.Trajectory, trajectory_filename: str) -> float:
    """
    Write the results of a nonequilibrium switching trajectory to a file. The trajectory is written to an
    mdtraj hdf5 file, whereas the cumulative work is written to a numpy file.

    Parameters
    ----------
    nonequilibrium_result : NonequilibriumResult namedtuple
        The result of a nonequilibrium switching calculation
    nonequilibrium_trajectory : md.Trajectory
        The trajectory resulting from a nonequilibrium simulation
    trajectory_filename : str
        The full filepath for where to store the trajectory

    Returns
    -------
    final_work : float
        The final value of the work trajectory
    """
    if nonequilibrium_trajectory is not None:
        nonequilibrium_trajectory.save_hdf5(trajectory_filename)

    return nonequilibrium_result.cumulative_work[-1]
Esempio n. 25
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    def test_py_cpu_smoke(self):

        traj = Trajectory.load(ref_file('ala2.pdb'))
        
        num_molecules = 1
        detector = xray.Detector.generic()
        detector.beam.photons_scattered_per_shot = 1e3

        I = scatter.simulate_shot(traj, num_molecules, detector, 
                                  finite_photon=True)
                                          
        # simple statistical sanity check
        assert np.abs(I.sum() - detector.beam.photons_scattered_per_shot) < \
                           np.sqrt(detector.beam.photons_scattered_per_shot)*6.0
Esempio n. 26
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    def test_python_call(self):
        """
        Test the GPU scattering simulation interface (scatter.simulate)
        """

        if not GPU: raise SkipTest
        print "testing python wrapper fxn..."
        
        traj = Trajectory.load(ref_file('ala2.pdb'))
        num_molecules = 512
        detector = xray.Detector.generic()

        py_I = scatter.simulate_shot(traj, num_molecules, detector)

        assert not np.all( py_I == 0.0 )
        assert not np.isnan(np.sum( py_I ))
Esempio n. 27
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 def setup(self):
     
     self.q_values = np.array([1.0, 2.0])
     self.num_phi  = 360
     self.l = 50.0
     self.d = xray.Detector.generic(spacing=0.4, l=self.l)
     self.t = Trajectory.load(ref_file('ala2.pdb'))
     
     self.num_shots = 2
     intensities = np.abs(np.random.randn(self.num_shots, self.d.num_pixels))
     io.saveh('tmp_tables.h5', data=intensities)
     
     self.tables_file = tables.File('tmp_tables.h5')
     self.i = self.tables_file.root.data
     
     self.shot = xray.Shotset(self.i, self.d)
     
     return
Esempio n. 28
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def test_no_hydrogens():
    
    traj = Trajectory.load(ref_file('ala2.pdb'))
    
    num_molecules = 1
    detector = xray.Detector.generic()
    detector.beam.photons_scattered_per_shot = 1e3

    I_noH = scatter.simulate_shot(traj, num_molecules, detector, 
                                  ignore_hydrogens=True,
                                  dont_rotate=True)
    I_wH  = scatter.simulate_shot(traj, num_molecules, detector, 
                                  ignore_hydrogens=False,
                                  dont_rotate=True)
                                  
    assert not np.all(I_noH == I_wH)
    
    # compute the differece -- we're not setting random numbers here so just
    # looking at radially averaged stuff...
    diff = np.sum(np.abs(I_noH - I_wH) / I_wH) / float(len(I_wH))
    print diff
    assert diff < 1.0, 'ignoring hydrogens makes too big of a difference...'
Esempio n. 29
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def test_lprmsd_null():
    ref = random.randn(1, 10, 3).astype(np.float32)
    new = np.copy(ref)

    value = lprmsd(Trajectory(xyz=new, topology=None), Trajectory(xyz=ref, topology=None))
    eq(value, np.array([0.0], dtype=np.float32), decimal=3)
Esempio n. 30
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def cg_by_index(trj,
                atom_indices_list,
                bead_label_list,
                chain_list=None,
                segment_id_list=None,
                resSeq_list=None,
                inplace=False,
                bonds=None,
                split_shared_atoms=False,
                mod_weights_list=None,
                mapping_function="com",
                charge_tol=1e-5,
                center_postwrap=False):
    """Create a coarse grained (CG) trajectory from subsets of atoms by
        computing centers of mass of selected sets of atoms.
    Parameters
    ----------
    atom_indices_list :
        list of array-like, dtype=int, shape=(n_beads,n_atoms)
        List of indices of atoms to combine into CG sites
    bead_label_list :
        list of maximum 4-letter strings to label CG sites
    chain_list :
        optional list of chain id's to split resulting beads into separate
        chains
    resSeq_list :
        optional list of residue sequence id's to assign cg residues
    segment_id_list :
        optional list of segment id's to assign cg residues
    inplace :
        bool, default=False
        If ``True``, the operation is done inplace, modifying ``trj``.
        Otherwise, a copy is returned with the sliced atoms, and
        ``trj`` is not modified.
    bonds : array-like,dtype=int, shape=(n_bonds,2), default=None
        If specified, sets these bonds in new topology
    split_shared_atoms: boolean
        If specified, check to see if atoms are shared per molecule in beads. If
        so, equally divide their weight accordingly for each bead.
    mapping_function: string, default='com': how to map xyz coordinates
        options: %s
    center_postwrap: Boolean
        Whether to wrap the CG system after it is mapped. Assumes that box is
        centered at 0, and only has effect if periodic information is present.

    Note - If repeated resSeq values are used, as for a repeated motiff
        in a CG polymer, those sections most be broken into separate
        chains or an incorrect topology will result

    Returns
    -------
    traj : md.Trajectory
        The return value is either ``trj``, or the new trajectory,
        depending on the value of ``inplace``.
    """ % mapping_options.keys()

    if not len(atom_indices_list) == len(bead_label_list):
        raise ValueError("Must supply a list of bead labels of the "
                         "same length as a list of selected atom indices")
    for bead_label in bead_label_list:
        if not (type(bead_label) is
                str) or len(bead_label) > 4 or len(bead_label) < 1:
            raise ValueError("Specified bead label '%s' is not valid, \
                             must be a string between 1 and 4 characters" %
                             bead_label)

    bead_label_list = [bead_label.upper() for bead_label in bead_label_list]

    if mapping_function not in mapping_options:
        raise ValueError("Must select a mapping function from: %s"\
                         %mapping_options.keys())

    if chain_list is None:
        chain_list = np.ones(len(atom_indices_list), dtype=int)
    elif len(chain_list) != len(atom_indices_list):
        raise ValueError("Supplied chain_list must be of the same length "
                         "as a list of selected atom indices")

    if segment_id_list is not None and len(segment_id_list) != len(
            atom_indices_list):
        raise ValueError("Supplied segment_id_list must be of the same "
                         "length as a list of selected atom indices")

    if resSeq_list is not None and len(resSeq_list) != len(atom_indices_list):
        raise ValueError("Supplied resSeq_list must be of the same "
                         "length as a list of selected atom indices")

    n_beads = len(atom_indices_list)

    xyz = np.zeros((trj.xyz.shape[0], n_beads, trj.xyz.shape[2]),
                   dtype=trj.xyz.dtype,
                   order='C')

    forces = np.zeros((trj.xyz.shape[0], n_beads, trj.xyz.shape[2]),
                      dtype=np.double,
                      order='C')

    columns = ["serial", "name", "element", "resSeq", "resName", "chainID"]

    #total masse for each cg bead.
    masses = np.zeros((n_beads), dtype=np.float64)
    #list of masses for elements in cg bead.
    masses_i = []
    #masses
    for ii in range(n_beads):
        #atoms in curent cg bead.
        atom_indices = atom_indices_list[ii]
        #first, construct lists of masses in current cg bead.
        temp_masses = np.array([])
        for jj in atom_indices:
            temp_masses = np.append(temp_masses, trj.top.atom(jj).element.mass)

        masses_i.append(temp_masses)
        masses[ii] = masses_i[ii].sum()

    if hasattr(trj.top.atom(1), 'charge'):
        #total charge for each cg bead.
        charges = np.zeros((n_beads), dtype=np.float64)
        #lists of charges for in current cg bead
        charges_i = []

        #charges
        for ii in range(n_beads):

            #atoms in curent cg bead.
            atom_indices = atom_indices_list[ii]

            #first, construct lists of masses in current cg bead.
            temp_charges = np.array([])

            for jj in atom_indices:
                temp_charges = np.append(temp_charges, trj.top.atom(jj).charge)

            charges_i.append(temp_charges)
            charges[ii] = charges_i[ii].sum()

    forcenorm_i = []
    if mapping_function == 'cof' or mapping_function == 'center_of_force':
        for ii in range(n_beads):
            atom_indices = atom_indices_list[ii]
            forcenorm_i.append(get_forcenorms(trj, atom_indices))

    if mapping_function == 'coc' or mapping_function == 'center_of_charge':
        for charge in charges:
            if np.absolute(charge) < charge_tol:
                raise ValueError("Total charge on site %i is near zero" % ii)

    topology_labels = []
    element_label_dict = {}

    if (split_shared_atoms):
        mod_weights_list = gen_unique_overlap_mod_weights(atom_indices_list)

    has_forces = False
    try:
        trj.__dict__['forces']
        test_forces = map_forces(trj, (0, ))
        has_forces = True
    except TypeError:
        print("WARNING: Invalid Forces\nNo Map applied to forces")
    except KeyError:
        pass
    except:
        print("Unknown error, check your forces\nexiting...")
        raise

    for i in range(n_beads):
        atom_indices = atom_indices_list[i]
        bead_label = bead_label_list[i]
        xyz_i = xyz[:, i, :]

        if mapping_function == 'coc' or mapping_function == 'center_of_charge':
            weights = charges_i[i]
        elif mapping_function == 'com' or mapping_function == 'center_of_mass':
            weights = masses_i[i]
        elif mapping_function == 'cof' or mapping_function == 'center_of_force':
            weights = forcenorm_i[i]
        elif mapping_function == 'center':
            weights = np.ones(len(atom_indices))

        if (mod_weights_list is not None):
            weights[:] = np.multiply(weights, mod_weights_list[i])

        compute_center_weighted(xyz_i,
                                trj.xyz,
                                atom_indices,
                                weights,
                                unitcell_lengths=trj.unitcell_lengths,
                                center_postwrap=center_postwrap)

        if has_forces:
            forces_i = map_forces(trj, atom_indices)
            forces[:, i, :] = forces_i

        if resSeq_list is not None:
            resSeq = resSeq_list[i]
        else:
            resSeq = i + 1

        #element_label='%4s'%('B%i'%(resSeq))
        if not bead_label in element_label_dict:
            element_label = '%2s' % ('B%i' % (len(element_label_dict) % 10))
            element_label_dict[bead_label] = element_label
        else:
            element_label = element_label_dict[bead_label]

        if element_label.strip().upper(
        ) not in element.Element._elements_by_symbol:
            element.Element(1000 + resSeq, element_label, element_label,
                            masses[i], 1.0)

        topology_labels.append([
            i, bead_label, element_label, resSeq,
            '%3s' % bead_label, chain_list[i]
        ])

    df = pd.DataFrame(topology_labels, columns=columns)
    topology = Topology.from_dataframe(df, bonds=bonds)

    if segment_id_list is not None:
        for beadidx, bead in enumerate(topology.atoms):
            bead.residue.segment_id = segment_id_list[beadidx]

    if inplace:
        if trj._topology is not None:
            trj._topology = topology
        trj._xyz = xyz

        return trj

    unitcell_lengths = unitcell_angles = None
    if trj._have_unitcell:
        unitcell_lengths = trj._unitcell_lengths.copy()
        unitcell_angles = trj._unitcell_angles.copy()

    time = trj._time.copy()

    new_trj = Trajectory(xyz=xyz,
                         topology=topology,
                         time=time,
                         unitcell_lengths=unitcell_lengths,
                         unitcell_angles=unitcell_angles)

    new_trj.forces = forces
    return new_trj
Esempio n. 31
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def load_pdb(filename, stride=None, atom_indices=None, frame=None,
             no_boxchk=False):
    """Load a RCSB Protein Data Bank file from disk.

    Parameters
    ----------
    filename : str
        Path to the PDB file on disk. The string could be a URL. Valid URL
        schemes include http and ftp.
    stride : int, default=None
        Only read every stride-th model from the file
    atom_indices : array_like, default=None
        If not None, then read only a subset of the atoms coordinates from the
        file. These indices are zero-based (not 1 based, as used by the PDB
        format). So if you want to load only the first atom in the file, you
        would supply ``atom_indices = np.array([0])``.
    frame : int, default=None
        Use this option to load only a single frame from a trajectory on disk.
        If frame is None, the default, the entire trajectory will be loaded.
        If supplied, ``stride`` will be ignored.
    no_boxchk : bool, default=False
        By default, a heuristic check based on the particle density will be
        performed to determine if the unit cell dimensions are absurd. If the
        particle density is >1000 atoms per nm^3, the unit cell will be
        discarded. This is done because all PDB files from RCSB contain a CRYST1
        record, even if there are no periodic boundaries, and dummy values are
        filled in instead. This check will filter out those false unit cells and
        avoid potential errors in geometry calculations. Set this variable to
        ``True`` in order to skip this heuristic check.

    Returns
    -------
    trajectory : md.Trajectory
        The resulting trajectory, as an md.Trajectory object.
        
    Examples
    --------
    >>> import mdtraj as md
    >>> pdb = md.load_pdb('2EQQ.pdb')
    >>> print(pdb)
    <mdtraj.Trajectory with 20 frames, 423 atoms at 0x110740a90>

    See Also
    --------
    mdtraj.PDBTrajectoryFile : Low level interface to PDB files
    """
    from mdtraj import Trajectory
    if not isinstance(filename, six.string_types):
        raise TypeError('filename must be of type string for load_pdb. '
            'you supplied %s' % type(filename))

    atom_indices = cast_indices(atom_indices)
    
    filename = str(filename)
    with PDBTrajectoryFile(filename) as f:
        atom_slice = slice(None) if atom_indices is None else atom_indices
        if frame is not None:
            coords = f.positions[[frame], atom_slice, :]
        else:
            coords = f.positions[::stride, atom_slice, :]
        assert coords.ndim == 3, 'internal shape error'
        n_frames = len(coords)

        topology = f.topology
        if atom_indices is not None:
            topology = topology.subset(atom_indices)

        if f.unitcell_angles is not None and f.unitcell_lengths is not None:
            unitcell_lengths = np.array([f.unitcell_lengths] * n_frames)
            unitcell_angles = np.array([f.unitcell_angles] * n_frames)
        else:
            unitcell_lengths = None
            unitcell_angles = None

        in_units_of(coords, f.distance_unit, Trajectory._distance_unit, inplace=True)
        in_units_of(unitcell_lengths, f.distance_unit, Trajectory._distance_unit, inplace=True)

    time = np.arange(len(coords))
    if frame is not None:
        time *= frame
    elif stride is not None:
        time *= stride

    traj = Trajectory(xyz=coords, time=time, topology=topology,
                      unitcell_lengths=unitcell_lengths,
                      unitcell_angles=unitcell_angles)

    if not no_boxchk and traj.unitcell_lengths is not None:
        # Only one CRYST1 record is allowed, so only do this check for the first
        # frame. Some RCSB PDB files do not *really* have a unit cell, but still
        # have a CRYST1 record with a dummy definition. These boxes are usually
        # tiny (e.g., 1 A^3), so check that the particle density in the unit
        # cell is not absurdly high. Standard water density is ~55 M, which
        # yields a particle density ~100 atoms per cubic nm. It should be safe
        # to say that no particle density should exceed 10x that.
        particle_density = traj.top.n_atoms / traj.unitcell_volumes[0]
        if particle_density > 1000:
            warnings.warn('Unlikely unit cell vectors detected in PDB file likely '
                          'resulting from a dummy CRYST1 record. Discarding unit '
                          'cell vectors.')
            traj._unitcell_lengths = traj._unitcell_angles = None

    return traj
Esempio n. 32
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#!/usr/bin/env python

import mdtraj
from mdtraj import Trajectory as t
import os

# combine trajectories
trjs = [f for f in os.listdir('.') if 'trj' in f]
ts = {}
tn = []
for i in trjs:
    num = i.split('.')[0].split('j')[-1]
    tn.append(num)
    ts[i] = t.load('./trj%i.h5' % int(num))
tn.sort()
z = ts['trj0.h5']
for i in tn:
    z = z.join(ts['trj%s.h5' % i])

# trim data to have a frame every 1 ns
frames = []
for i in range(len(z)):
    if i % 10 == 0:
        frames.append(i)
zp = z.slice(frames)

# save combined data
zp.save_pdb('ns.pdb')
Esempio n. 33
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def load_pdb(filename, stride=None, atom_indices=None, frame=None):
    """Load a RCSB Protein Data Bank file from disk.

    Parameters
    ----------
    filename : str
        Path to the PDB file on disk. The string could be a URL. Valid URL
        schemes include http and ftp.
    stride : int, default=None
        Only read every stride-th model from the file
    atom_indices : array_like, optional
        If not none, then read only a subset of the atoms coordinates from the
        file. These indices are zero-based (not 1 based, as used by the PDB
        format). So if you want to load only the first atom in the file, you
        would supply ``atom_indices = np.array([0])``.
    frame : int, optional
        Use this option to load only a single frame from a trajectory on disk.
        If frame is None, the default, the entire trajectory will be loaded.
        If supplied, ``stride`` will be ignored.

    Returns
    -------
    trajectory : md.Trajectory
        The resulting trajectory, as an md.Trajectory object.
        
    Examples
    --------
    >>> import mdtraj as md
    >>> pdb = md.load_pdb('2EQQ.pdb')
    >>> print pdb
    <mdtraj.Trajectory with 20 frames, 423 atoms at 0x110740a90>

    See Also
    --------
    mdtraj.PDBTrajectoryFile : Low level interface to PDB files
    """
    from mdtraj import Trajectory
    if not isinstance(filename, six.string_types):
        raise TypeError('filename must be of type string for load_pdb. '
                        'you supplied %s' % type(filename))

    atom_indices = cast_indices(atom_indices)

    filename = str(filename)
    with PDBTrajectoryFile(filename) as f:
        atom_slice = slice(None) if atom_indices is None else atom_indices
        if frame is not None:
            coords = f.positions[[frame], atom_slice, :]
        else:
            coords = f.positions[::stride, atom_slice, :]
        assert coords.ndim == 3, 'internal shape error'
        n_frames = len(coords)

        topology = f.topology
        if atom_indices is not None:
            topology = topology.subset(atom_indices)

        if f.unitcell_angles is not None and f.unitcell_lengths is not None:
            unitcell_lengths = np.array([f.unitcell_lengths] * n_frames)
            unitcell_angles = np.array([f.unitcell_angles] * n_frames)
        else:
            unitcell_lengths = None
            unitcell_angles = None

        in_units_of(coords,
                    f.distance_unit,
                    Trajectory._distance_unit,
                    inplace=True)
        in_units_of(unitcell_lengths,
                    f.distance_unit,
                    Trajectory._distance_unit,
                    inplace=True)

    time = np.arange(len(coords))
    if frame is not None:
        time *= frame
    elif stride is not None:
        time *= stride

    return Trajectory(xyz=coords,
                      time=time,
                      topology=topology,
                      unitcell_lengths=unitcell_lengths,
                      unitcell_angles=unitcell_angles)
Esempio n. 34
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 def build_scaled_pdb(traj: md.Trajectory, ratio, pdb_name):
     f_rescale = np.vectorize(lambda t: t * ratio)
     traj.xyz[0] = f_rescale(traj.xyz[0])
     scaled_pdb_name = PDBAnalyzer.prefix_scaled_path + pdb_name
     traj.save_pdb(filename=scaled_pdb_name)
Esempio n. 35
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def cg_by_index(trj,
                atom_indices_list,
                bead_label_list,
                chain_list=None,
                segment_id_list=None,
                resSeq_list=None,
                inplace=False,
                bonds=None,
                mapping_function="com"):
    """Create a coarse grained (CG) trajectory from subsets of atoms by 
        computing centers of mass of selected sets of atoms.
    Parameters
    ----------
    atom_indices_list : list of array-like, dtype=int, shape=(n_beads,n_atoms)
        List of indices of atoms to combine into CG sites
    bead_label_list : list of maximum 4-letter strings to label CG sites
    chain_list : optional list of chain id's to split resulting beads into separate chains
    resSeq_list : optional list of residue sequence id's to assign cg residues
    segment_id_list : optional list of segment id's to assign cg residues
    inplace : bool, default=False
        If ``True``, the operation is done inplace, modifying ``trj``.
        Otherwise, a copy is returned with the sliced atoms, and
        ``trj`` is not modified.
    bonds : array-like,dtype=int, shape=(n_bonds,2), default=None
        If specified, sets these bonds in new topology 
    mapping_function: string, default='com': how to map xyz coordinates
        options: %s

    Note - If repeated resSeq values are used, as for a repeated motiff in a CG polymer, 
        those sections most be broken into separate chains or an incorrect topology will result
 
    Returns
    -------
    traj : md.Trajectory
        The return value is either ``trj``, or the new trajectory,
        depending on the value of ``inplace``.
    """ % mapping_options.keys()
    if not len(atom_indices_list) == len(bead_label_list):
        raise ValueError(
            "Must supply a list of bead labels of the same length as a list of selected atom indices"
        )
    for bead_label in bead_label_list:
        if not (type(bead_label) is
                str) or len(bead_label) > 4 or len(bead_label) < 1:
            raise ValueError(
                "Specified bead label '%s' is not valid, must be a string between 1 and 4 characters"
                % bead_label)
    bead_label_list = [bead_label.upper() for bead_label in bead_label_list]

    if mapping_function not in mapping_options:
        raise ValueError("Must select a mapping function from: %s" %
                         mapping_options.keys())
    map_coords = mapping_options[mapping_function]

    if chain_list is None:
        chain_list = np.ones(len(atom_indices_list), dtype=int)
    elif len(chain_list) != len(atom_indices_list):
        raise ValueError(
            "Supplied chain_list must be of the same length as a list of selected atom indices"
        )

    if segment_id_list is not None and len(segment_id_list) != len(
            atom_indices_list):
        raise ValueError(
            "Supplied segment_id_list must be of the same length as a list of selected atom indices"
        )

    if resSeq_list is not None and len(resSeq_list) != len(atom_indices_list):
        raise ValueError(
            "Supplied resSeq_list must be of the same length as a list of selected atom indices"
        )

    n_beads = len(atom_indices_list)
    xyz = np.zeros((trj.xyz.shape[0], n_beads, trj.xyz.shape[2]),
                   dtype=trj.xyz.dtype,
                   order='C')
    forces = np.zeros((trj.xyz.shape[0], n_beads, trj.xyz.shape[2]),
                      dtype=np.double,
                      order='C')
    columns = ["serial", "name", "element", "resSeq", "resName", "chainID"]
    masses = np.array([
        np.sum([a.mass for a in trj.top.atoms if a.index in atom_indices])
        for atom_indices in atom_indices_list
    ],
                      dtype=np.float64)
    charges = np.array([
        np.sum([a.charge for a in trj.top.atoms if a.index in atom_indices])
        for atom_indices in atom_indices_list
    ],
                       dtype=np.float64)

    topology_labels = []
    element_label_dict = {}

    xyz_i = np.zeros((trj.xyz.shape[0], trj.xyz.shape[2]),
                     dtype=trj.xyz.dtype,
                     order='C')

    for i in range(n_beads):
        atom_indices = atom_indices_list[i]
        bead_label = bead_label_list[i]
        #xyz_i = map_coords(trj,atom_indices)

        masses_i = np.array(
            [a.mass for a in trj.top.atoms if a.index in atom_indices_list[i]],
            dtype=np.float64)

        map_coords(xyz_i,
                   trj.xyz,
                   atom_indices,
                   masses_i,
                   unitcell_lengths=trj.unitcell_lengths)

        xyz[:, i, :] = xyz_i

        if "forces" in trj.__dict__ and len(trj.forces) > 0:
            forces_i = map_forces(trj, atom_indices)
            forces[:, i, :] = forces_i

        if resSeq_list is not None:
            resSeq = resSeq_list[i]
        else:
            resSeq = i + 1

        #element_label='%4s'%('B%i'%(resSeq))
        if not bead_label in element_label_dict:
            element_label = '%2s' % ('B%i' % (len(element_label_dict) % 10))
            element_label_dict[bead_label] = element_label
        else:
            element_label = element_label_dict[bead_label]

        if element_label.strip().upper(
        ) not in element.Element._elements_by_symbol:
            element.Element(1000 + resSeq, element_label, element_label,
                            masses[i], 1.0)

        topology_labels.append([
            i, bead_label, element_label, resSeq,
            '%3s' % bead_label, chain_list[i]
        ])

    df = pd.DataFrame(topology_labels, columns=columns)
    topology = Topology.from_dataframe(df, bonds=bonds)

    if segment_id_list is not None:
        for beadidx, bead in enumerate(topology.atoms):
            bead.residue.segment_id = segment_id_list[beadidx]

    if inplace:
        if trj._topology is not None:
            trj._topology = topology
        trj._xyz = xyz

        return trj

    unitcell_lengths = unitcell_angles = None
    if trj._have_unitcell:
        unitcell_lengths = trj._unitcell_lengths.copy()
        unitcell_angles = trj._unitcell_angles.copy()
    time = trj._time.copy()

    new_trj = Trajectory(xyz=xyz,
                         topology=topology,
                         time=time,
                         unitcell_lengths=unitcell_lengths,
                         unitcell_angles=unitcell_angles)
    new_trj.forces = forces
    return new_trj
Esempio n. 36
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def cg_by_index(trj, atom_indices_list, bead_label_list, chain_list=None, segment_id_list=None, resSeq_list=None, inplace=False, bonds=None, mapping_function="com"):
    """Create a coarse grained (CG) trajectory from subsets of atoms by 
        computing centers of mass of selected sets of atoms.
    Parameters
    ----------
    atom_indices_list : list of array-like, dtype=int, shape=(n_beads,n_atoms)
        List of indices of atoms to combine into CG sites
    bead_label_list : list of maximum 4-letter strings to label CG sites
    chain_list : optional list of chain id's to split resulting beads into separate chains
    resSeq_list : optional list of residue sequence id's to assign cg residues
    segment_id_list : optional list of segment id's to assign cg residues
    inplace : bool, default=False
        If ``True``, the operation is done inplace, modifying ``trj``.
        Otherwise, a copy is returned with the sliced atoms, and
        ``trj`` is not modified.
    bonds : array-like,dtype=int, shape=(n_bonds,2), default=None
        If specified, sets these bonds in new topology 
    mapping_function: string, default='com': how to map xyz coordinates
        options: %s

    Note - If repeated resSeq values are used, as for a repeated motiff in a CG polymer, 
        those sections most be broken into separate chains or an incorrect topology will result
 
    Returns
    -------
    traj : md.Trajectory
        The return value is either ``trj``, or the new trajectory,
        depending on the value of ``inplace``.
    """%mapping_options.keys()
    if not len(atom_indices_list)==len(bead_label_list):
        raise ValueError("Must supply a list of bead labels of the same length as a list of selected atom indices")
    for bead_label in bead_label_list:
        if not (type(bead_label) is str) or len(bead_label)>4 or len(bead_label)<1:
            raise ValueError("Specified bead label '%s' is not valid, must be a string between 1 and 4 characters"%bead_label)
    bead_label_list = [ bead_label.upper() for bead_label in bead_label_list ]

    if mapping_function not in mapping_options:
        raise ValueError("Must select a mapping function from: %s"%mapping_options.keys())
    map_coords = mapping_options[mapping_function]

    if chain_list is None:
        chain_list = np.ones(len(atom_indices_list),dtype=int)
    elif len(chain_list)!=len(atom_indices_list):
        raise ValueError("Supplied chain_list must be of the same length as a list of selected atom indices")

    if segment_id_list is not None and len(segment_id_list)!=len(atom_indices_list):
        raise ValueError("Supplied segment_id_list must be of the same length as a list of selected atom indices")

    if resSeq_list is not None and len(resSeq_list)!=len(atom_indices_list):
        raise ValueError("Supplied resSeq_list must be of the same length as a list of selected atom indices")

    n_beads = len(atom_indices_list)
    xyz = np.zeros((trj.xyz.shape[0],n_beads,trj.xyz.shape[2]),dtype=trj.xyz.dtype,order='C')
    forces = np.zeros((trj.xyz.shape[0],n_beads,trj.xyz.shape[2]),dtype=np.double,order='C')
    columns = ["serial","name","element","resSeq","resName","chainID"]
    masses = np.array([  np.sum([a.mass for a in trj.top.atoms if a.index in atom_indices]) for atom_indices in atom_indices_list],dtype=np.float64)
    charges = np.array([  np.sum([a.charge for a in trj.top.atoms if a.index in atom_indices]) for atom_indices in atom_indices_list],dtype=np.float64)

    topology_labels = []
    element_label_dict = {}

    xyz_i = np.zeros((trj.xyz.shape[0],trj.xyz.shape[2]),dtype=trj.xyz.dtype,order='C')

    for i in range(n_beads):
        atom_indices = atom_indices_list[i]
        bead_label = bead_label_list[i]
        #xyz_i = map_coords(trj,atom_indices)

        masses_i = np.array([a.mass for a in trj.top.atoms if a.index in atom_indices_list[i]],dtype=np.float64)

        map_coords(xyz_i,trj.xyz,atom_indices,masses_i,unitcell_lengths=trj.unitcell_lengths)

        xyz[:,i,:] = xyz_i

        if "forces" in trj.__dict__ and len(trj.forces)>0:
            forces_i = map_forces(trj,atom_indices)
            forces[:,i,:] = forces_i

        if resSeq_list is not None:
            resSeq = resSeq_list[i]
        else:
            resSeq = i + 1 

        #element_label='%4s'%('B%i'%(resSeq))
        if not bead_label in element_label_dict:
            element_label='%2s'%('B%i'%(len(element_label_dict)%10))
            element_label_dict[bead_label] = element_label
        else:
            element_label = element_label_dict[bead_label]

        if element_label.strip().upper() not in element.Element._elements_by_symbol:
            element.Element(1000+resSeq, element_label, element_label, masses[i], 1.0)

        topology_labels.append( [i,bead_label,element_label,resSeq,'%3s'%bead_label,chain_list[i]] )

    df = pd.DataFrame(topology_labels,columns=columns)
    topology = Topology.from_dataframe(df,bonds=bonds)
    
    if segment_id_list is not None:
        for beadidx,bead in enumerate(topology.atoms):
            bead.residue.segment_id = segment_id_list[beadidx]
        
    if inplace:
        if trj._topology is not None:
            trj._topology = topology
        trj._xyz = xyz

        return trj

    unitcell_lengths = unitcell_angles = None
    if trj._have_unitcell:
        unitcell_lengths = trj._unitcell_lengths.copy()
        unitcell_angles = trj._unitcell_angles.copy()
    time = trj._time.copy()

    new_trj = Trajectory(xyz=xyz, topology=topology, time=time,
                      unitcell_lengths=unitcell_lengths,
                      unitcell_angles=unitcell_angles)
    new_trj.forces = forces
    return new_trj
Esempio n. 37
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File: vmd.py Progetto: bkmi/utilsb
 def save_trajectory(trajectory: mdtraj.Trajectory,
                     filename: str,
                     force_overwrite: bool = True):
     trajectory.save_pdb(filename, force_overwrite=force_overwrite)
Esempio n. 38
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parser.add_argument('--pro', action='store_true', help='write trj of protein only', default=False)
parser.add_argument('--sr', type=str, help='script root', default='/home/kmckiern/scripts/')
args = parser.parse_args()

sr = args.sr
sys.path.insert(0, sr + 'py_general/')
from toolz import natural_sort

ext_i = '.' + args.trj_ext
td = args.trj_dir

# combine trajectories
trjs = [f for f in os.listdir(td) if ext_i in f] 
trjs = natural_sort(trjs)

ts = t.load(trjs[0], top=args.top, stride=args.stride1)
if args.vs:
    # i'm going to pad these residues by 8
    arg = ts.top.select('resid 23 to 39')
    lysglu = ts.top.select('resid 118 to 135')
    lys = ts.top.select('resid 308 to 324')
    vs = np.concatenate([arg, lysglu, lys])
    ts = ts.atom_slice(vs)
    try:
        ts[0].save_pdb('/home/kmckiern/clc/analysis/vs_dihed/pro/vs_ref.pdb')
    except:
        print 'usual protonation error, probably'
    nt = len(trjs)
    for ndx, i in enumerate(trjs[1:]):
        # for newest trj, cut end just in case write is incomplete
        if ndx + 1 == nt:
Esempio n. 39
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def render_traj(topology, positions):
    traj = Trajectory(positions / unit.nanometers,
                      Topology.from_openmm(topology))
    return (show_mdtraj(traj).add_ball_and_stick('all').center_view(zoom=True))
Esempio n. 40
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def traj_frame_to_sampler_state(traj: md.Trajectory, frame_number: int,box_vectors):
    xyz = traj.xyz[frame_number, :, :]
    box_vectors = traj.openmm_boxes(frame_number)
    sampler_state = states.SamplerState(unit.Quantity(xyz, unit=unit.nanometers))
    return sampler_state