def test_director():
directors = order._compute_director(TRAJ2_LINE1)
assert eq(directors,
np.array([[1, 0, 0], # Frame 0
[0, 1, 0], # Frame 1
[0, 0, 1], # Frame 2
[1, 0, 0], # Frame 3
[1, 0, 0], # Frame 4
]))
directors = order._compute_director(TRAJ2_LINE2)
assert eq(directors,
np.array([[1, 0, 0], # Frame 0
[0, 1, 0], # Frame 1
[0, 0, 1], # Frame 2
[0, 1, 0], # Frame 3
[0, 0, 1], # Frame 4
]))
def calc_avg_tilt_angle(traj_filename, top_filename, output_filename,
ndx_filename, n_chains):
"""Calculate the avg. tilt angle of both monolayers in a two monolayer system
Returns the average tilt angle of each monolayer at each frame of a trajectory
in a dual monolayer system.
Parameters
----------
traj_filename : str
Name of trajectory file
top_filename : str
Name of topology file
output_filename : str
Name of output file
ndx_filename : str
Name of Gromacs .ndx file which specifies atom groups
n_chains : int
Number of chains per monolayer
"""
groups = read_ndx(ndx_filename)
bottom_chains = [id - 1 for id in groups['bottom_chains']]
bottom_chains = [
chain.tolist() for chain in np.array_split(bottom_chains, n_chains)
]
top_chains = [id - 1 for id in groups['top_chains']]
top_chains = [
chain.tolist() for chain in np.array_split(top_chains, n_chains)
]
traj = md.load(traj_filename, top=top_filename)
tilts = []
tilts_err = []
for monolayer in [bottom_chains, top_chains]:
monolayer_tilt = []
for i, chain in enumerate(monolayer):
chain_slice = traj.atom_slice(chain)
directors = _compute_director(chain_slice)
monolayer_tilt.append([
_tilt_angle(director, [0.0, 0.0, 1.0])
for director in directors
])
monolayer_tilt = np.asarray(monolayer_tilt)
monolayer_tilt = np.transpose(monolayer_tilt)
tilts_by_frame = np.mean(monolayer_tilt, axis=1)
tilts_err_by_frame = np.std(monolayer_tilt, axis=1)
tilts.append(tilts_by_frame)
tilts_err.append(tilts_err_by_frame)
tilts = np.asarray(tilts)
tilts = np.transpose(tilts)
np.savetxt(output_filename,
np.column_stack((traj.time, tilts)),
header='Time\tBottom\tTop')
def test_director(traj2):
traj2_line1 = traj2.atom_slice((0, 1))
traj2_line2 = traj2.atom_slice((2, 3))
directors = order._compute_director(traj2_line1)
assert eq(
directors,
np.array([
[1, 0, 0], # Frame 0
[0, 1, 0], # Frame 1
[0, 0, 1], # Frame 2
[1, 0, 0], # Frame 3
[1, 0, 0], # Frame 4
]))
directors = order._compute_director(traj2_line2)
assert eq(
directors,
np.array([
[1, 0, 0], # Frame 0
[0, 1, 0], # Frame 1
[0, 0, 1], # Frame 2
[0, 1, 0], # Frame 3
[0, 0, 1], # Frame 4
]))
def calc_avg_tilt_angle(traj_filename,
top_filename,
ndx_filename,
output_filename,
n_chains,
C_only=False):
"""Calculate the average tilt angle of a monolayer
Outputs the average tilt angle at each frame of a trajectory,
averaged between the top and bottom monolayers in a dual monolayer
system. Output is directed to a file with the filename specified by
the user.
Parameters
----------
traj_filename : str
Name of trajectory file (typically XTC format)
top_filename : str
Name of topology file (typically GRO format)
ndx_filename : str
Name of the GROMACS index file to read group information from
output_filename : str
Name of file to output results to
n_chains : int
Number of monolayer chains per surface
C_only : bool, optional, default=False
Only include carbon atoms in the calculation
Notes
-----
Only valid for single-component monolayers (assumes that all chains in a
monolayer contain the same number of atoms).
Only valid for systems featuring the same number of chains in each monolayer.
To-do
-----
Support for multi-component monolayers
Support for systems where each monolayer features an arbitrary number of chains
"""
bottom_chains, top_chains = _gather_chains(top_filename,
ndx_filename,
n_chains,
C_only=C_only)
traj = md.load(traj_filename, top=top_filename)
tilt_means = []
tilt_errs = []
for monolayer, normal in zip([bottom_chains, top_chains],
[[0.0, 0.0, 1.0], [0.0, 0.0, -1.0]]):
monolayer_tilt = []
for i, chain in enumerate(monolayer):
chain_slice = traj.atom_slice(chain)
directors = _compute_director(chain_slice)
monolayer_tilt.append(
[_tilt_angle(director, normal) for director in directors])
monolayer_tilt = np.transpose(monolayer_tilt)
tilt_means.append(np.mean(monolayer_tilt, axis=1))
tilt_errs.append(np.std(monolayer_tilt, axis=1))
tilt_means = np.transpose(tilt_means)
tilt_errs = np.transpose(tilt_errs)
np.savetxt(output_filename,
np.column_stack((traj.time, tilt_means, tilt_errs)),
header='Time\tBottom\tTop\tBottom_std\tTop_std')
def calc_OCF(traj_filename, top_filename, output_filetag, ndx_filename,
chainlength):
"""Calculate the orientational correlation function of monolayer chains
Parameters
----------
traj_filename : str
Name of (unwrapped) trajectory file
top_filename : str
Name of topology file
output_filename : str
Name of output file
"""
topology = md.load(top_filename).topology
atoms = np.array(list(topology.atoms))
atom_names = [atom.name for atom in atoms]
groups = read_ndx(ndx_filename)
bottom_chains = [
id - 1 for id in groups['bottom_chains'] if atom_names[id - 1] == 'C'
]
n_chains = int(len(bottom_chains) / chainlength)
bottom_chains = [
chain.tolist() for chain in np.array_split(bottom_chains, n_chains)
]
top_chains = [
id - 1 for id in groups['top_chains'] if atom_names[id - 1] == 'C'
]
top_chains = [
chain.tolist() for chain in np.array_split(top_chains, n_chains)
]
traj = md.load(traj_filename, top=top_filename)
directors = []
locations = []
for i, chain in enumerate(bottom_chains):
chain_slice = traj.atom_slice(chain)
chain_director = _compute_director(chain_slice)
for director in chain_director:
if director[-1] < 0:
director *= -1.
directors.append(chain_director)
com = md.compute_center_of_mass(chain_slice)
for com_frame in com:
for j in range(2):
if com_frame[j] < 0:
com_frame[j] += traj.unitcell_lengths[0, j]
elif com_frame[j] > traj.unitcell_lengths[0, j]:
com_frame[j] -= traj.unitcell_lengths[0, j]
locations.append(com)
directors = np.array(directors).transpose(1, 0, 2)
film_directors = np.mean(directors, axis=1)
film_directors /= np.linalg.norm(film_directors,
axis=1).reshape(len(film_directors), 1)
locations = np.array(locations).transpose(1, 0, 2)
com_traj = _create_com_traj(locations, traj.time, traj.unitcell_lengths,
traj.unitcell_angles)
pairs = com_traj.top.select_pairs('all', 'all')
distances = md.compute_distances(com_traj, pairs, periodic=True)
cof = []
cof_6 = []
for i, frame in enumerate(directors):
frame_angles = np.array(
[_angle(film_directors[i], director) for director in frame])
dist_hist = np.histogram(distances[i], bins=np.arange(0.25, 2.5, 0.1))
correlations = np.cos(
np.array([
2. * (frame_angles[pairs[j, 0]] - frame_angles[pairs[j, 1]])
for j, dist in enumerate(distances[i])
]))
correlation_hist = np.histogram(distances[i],
bins=np.arange(0.25, 2.5, 0.1),
weights=correlations)
correlations_6 = np.cos(
np.array([
6. * (frame_angles[pairs[j, 0]] - frame_angles[pairs[j, 1]])
for j, dist in enumerate(distances[i])
]))
correlation_hist_6 = np.histogram(distances[i],
bins=np.arange(0.25, 2.5, 0.1),
weights=correlations_6)
cof.append(correlation_hist[0] / dist_hist[0])
cof_6.append(correlation_hist_6[0] / dist_hist[0])
cof = np.array(cof)
cof = np.mean(cof, axis=0)
cof_6 = np.array(cof_6)
cof_6 = np.mean(cof_6, axis=0)
r_vals = np.arange(0.25, 2.5, 0.1)
r_vals = np.array([(val + r_vals[i + 1]) / 2
for i, val in enumerate(r_vals[:-1])])
np.savetxt(output_filetag + '2.txt', np.vstack((r_vals, cof)))
np.savetxt(output_filetag + '6.txt', np.vstack((r_vals, cof_6)))
def calc_avg_tilt_angle(traj_filename, top_filename, output_filename,
ndx_filename, chainlength):
"""Calculate the average tilt angle of a monolayer
Returns the average tilt angle at each frame of a trajectory,
averaged between the top and bottom monolayers in a dual monolayer
system.
Parameters
----------
traj_filename : str
Name of trajectory file
top_filename : str
Name of topology file
output_filename : str
Name of output file
ndx_filename : str
Name of Gromacs .ndx file which specifies atom groups
chainlength : int
Number of carbons per chain
"""
topology = md.load(top_filename).topology
atoms = np.array(list(topology.atoms))
atom_names = [atom.name for atom in atoms]
groups = read_ndx(ndx_filename)
bottom_chains = [
id - 1 for id in groups['bottom_chains'] if atom_names[id - 1] == 'C'
]
n_chains = int(len(bottom_chains) / chainlength)
bottom_chains = [
chain.tolist() for chain in np.array_split(bottom_chains, n_chains)
]
top_chains = [
id - 1 for id in groups['top_chains'] if atom_names[id - 1] == 'C'
]
top_chains = [
chain.tolist() for chain in np.array_split(top_chains, n_chains)
]
bottom_chemisorbed = [
id - 1 for id in groups['bottom_chemisorbed']
if atom_names[id - 1] == 'C'
]
n_chemisorbed = int(len(bottom_chemisorbed) / chainlength)
bottom_chemisorbed = [
chain.tolist()
for chain in np.array_split(bottom_chemisorbed, n_chemisorbed)
]
top_chemisorbed = [
id - 1 for id in groups['top_chemisorbed'] if atom_names[id - 1] == 'C'
]
top_chemisorbed = [
chain.tolist()
for chain in np.array_split(top_chemisorbed, n_chemisorbed)
]
n_crosslinked = n_chains - n_chemisorbed
if n_crosslinked > 0:
bottom_crosslinked = [
id - 1 for id in groups['bottom_crosslinked']
if atom_names[id - 1] == 'C'
]
bottom_crosslinked = [
chain.tolist()
for chain in np.array_split(bottom_crosslinked, n_crosslinked)
]
top_crosslinked = [
id - 1 for id in groups['top_crosslinked']
if atom_names[id - 1] == 'C'
]
top_crosslinked = [
chain.tolist()
for chain in np.array_split(top_crosslinked, n_crosslinked)
]
traj = md.load(traj_filename, top=top_filename)
if n_crosslinked > 0:
tilt_groups = [
bottom_chains, top_chains, bottom_chemisorbed, top_chemisorbed,
bottom_crosslinked, top_crosslinked
]
else:
tilt_groups = [
bottom_chains, top_chains, bottom_chemisorbed, top_chemisorbed
]
tilts = []
tilts_err = []
for group in tilt_groups:
group_tilt_angles = []
for i, chain in enumerate(group):
chain_slice = traj.atom_slice(chain)
directors = _compute_director(chain_slice)
group_tilt_angles.append([
_tilt_angle(director, [0.0, 0.0, 1.0])
for director in directors
])
group_tilt_angles = np.asarray(group_tilt_angles)
group_tilt_angles = np.transpose(group_tilt_angles)
tilts_by_frame = np.mean(group_tilt_angles, axis=1)
tilts_err_by_frame = np.std(group_tilt_angles, axis=1)
tilts.append(tilts_by_frame)
tilts_err.append(tilts_err_by_frame)
tilts_mean_chains = np.mean([tilts[0], tilts[1]], axis=0)
tilts_mean_chemisorbed = np.mean([tilts[2], tilts[3]], axis=0)
tilts.insert(2, tilts_mean_chains)
tilts.insert(5, tilts_mean_chemisorbed)
if n_crosslinked > 0:
tilts_mean_crosslinked = np.mean([tilts[6], tilts[7]], axis=0)
tilts.append(tilts_mean_crosslinked)
tilts = np.asarray(tilts)
tilts = np.transpose(tilts)
if n_crosslinked > 0:
np.savetxt(output_filename,
np.column_stack((traj.time, tilts)),
header='Time\tBottom\tTop\tAll-mean\tBottom-chemisorbed\t' +
'Top-chemisorbed\tChemisorbed-mean\tBottom-crosslinked\t' +
'Top-crosslinked\tCrosslinked-mean')
else:
np.savetxt(output_filename,
np.column_stack((traj.time, tilts)),
header='Time\tBottom\tTop\tAll-mean\tBottom-chemisorbed\t' +
'Top-chemisorbed\tChemisorbed-mean')