def calc_nematic_order(traj_filename, top_filename, output_filename,
ndx_filename, n_chains):
"""Calculate the nematic order of both monolayers in a two monolayer system
Returns the nematic order 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)
S2_bottom = md.compute_nematic_order(traj, indices=bottom_chains)
S2_top = md.compute_nematic_order(traj, indices=top_chains)
np.savetxt(output_filename,
np.column_stack((traj.time, S2_bottom, S2_top)),
header='Time\tBottom\tTop')
def calc_nematic_order(traj_filename,
top_filename,
ndx_filename,
output_filename,
n_chains,
C_only=False):
"""Calculate the nematic order of a monolayer
Outputs the average nematic order at each frame of a trajectory
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)
S2_bottom = md.compute_nematic_order(traj, indices=bottom_chains)
S2_top = md.compute_nematic_order(traj, indices=top_chains)
np.savetxt(output_filename,
np.column_stack((traj.time, S2_bottom, S2_top)),
header='Time\tBottom\tTop')
def test_order_from_traj():
"""Made a perfectly aligned monolayer, should have S2 = 1
"""
traj = md.load(get_fn('alkane-monolayer.pdb'))
indices = [list(range(1900+x, 1900+x+36)) for x in range(0, 64*36, 36)]
s2 = md.compute_nematic_order(traj, indices=indices)
assert_allclose(1.0, s2)
def test_order_from_traj(get_fn):
"""Made a perfectly aligned monolayer, should have S2 = 1
"""
traj = md.load(get_fn('alkane-monolayer.pdb'))
indices = [
list(range(1900 + x, 1900 + x + 36)) for x in range(0, 64 * 36, 36)
]
s2 = md.compute_nematic_order(traj, indices=indices)
np.testing.assert_allclose(1.0, s2)
def calc_nematic_order(traj, lipid_dict):
"""Compute nematic order parameter
Parameters
----------
traj : mdTraj traj()
lipid_dict : dict()
dictionary apping residue indices to associated atom indices
Returns
-------
s2_ave : float
Average S2 over all frames
s2_std :
S2 standard dviation
s2_list :
Frame by frame list of S2 values
Notes
-----
Assumes 64 residues per leaflet
"""
top_chains = []
bot_chains = []
for i, key in enumerate(lipid_dict.keys()):
indices = [int(item) for item in lipid_dict[key]]
if i <= 63:
top_chains.append(indices)
else:
bot_chains.append(indices)
s2_top = mdtraj.compute_nematic_order(traj, indices=top_chains)
s2_bot = mdtraj.compute_nematic_order(traj, indices=bot_chains)
s2_list = (s2_top + s2_bot) / 2
#s2_blocks = s2_list[:-1].reshape(int((traj.n_frames-1)/250),250)
#s2_block_avgs = np.mean(s2_blocks, axis = 1)
s2_block_avgs = np.mean(s2_list)
s2_ave = np.mean(s2_block_avgs)
#s2_std = np.std(s2_block_avgs)
s2_std = np.std(s2_list)
return s2_ave, s2_std, s2_list
def calc_moving_s2(traj, tail_groups, selected_keys, window_size=3):
""" Compute S2 over a moving window so you can look at different segments
Parameters
----------
traj : MDTraj trajectory
tail_groups : dict
Keys: residue IDs
Values: atom indices
selected_keys: list
residue IDs so we can pick out specific residue chains
window_size: int, default 3
number of consecutive carbons to look at for S2
Returns
-------
all_s2: array, traj.n_frames x n_carbons - window_size
Each row is the nematic order for a frame
Each column is the nematic order for that selection of chains.
i.e. column 0 looks at the S2 for carbon[0,window_size)
"""
n_carbons = len([
a for a in tail_groups[selected_keys[0]]
if 'H' != traj.topology.atom(a).name[0]
])
all_s2 = np.zeros((traj.n_frames, n_carbons - 3))
for i, carbon_start in enumerate(range(n_carbons - 3)):
tail_no_h = {
key: [
int(a) for a in tail_groups[key]
if 'H' != traj.topology.atom(a).name[0]
]
for key in tail_groups.keys()
}
sub_tails = [
tail_no_h[key][carbon_start:carbon_start + 3]
for key in selected_keys
]
all_s2[:, i] = mdtraj.compute_nematic_order(traj, indices=sub_tails)
return all_s2
def calc_nematic_order(traj_filename, top_filename, output_filename,
ndx_filename, chainlength):
"""Calculate the nematic order of a monolayer
Returns the average nematic order at each frame of a trajectory
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)
S2_bottom = md.compute_nematic_order(traj, indices=bottom_chains)
S2_top = md.compute_nematic_order(traj, indices=top_chains)
S2_bottom_chemisorbed = md.compute_nematic_order(
traj, indices=bottom_chemisorbed)
S2_top_chemisorbed = md.compute_nematic_order(traj,
indices=top_chemisorbed)
if n_crosslinked > 0:
S2_bottom_crosslinked = md.compute_nematic_order(
traj, indices=bottom_crosslinked)
S2_top_crosslinked = md.compute_nematic_order(traj,
indices=top_crosslinked)
S2_mean_crosslinked = np.mean(
[S2_bottom_crosslinked, S2_top_crosslinked], axis=0)
S2_mean_chains = np.mean([S2_bottom, S2_top], axis=0)
S2_mean_chemisorbed = np.mean([S2_bottom_chemisorbed, S2_top_chemisorbed],
axis=0)
if n_crosslinked > 0:
np.savetxt(output_filename,
np.column_stack(
(traj.time, S2_bottom, S2_top, S2_mean_chains,
S2_bottom_chemisorbed, S2_top_chemisorbed,
S2_mean_chemisorbed, S2_bottom_crosslinked,
S2_top_crosslinked, S2_mean_crosslinked)),
header='Time\tBottom\tTop\tAll-mean\t' +
'Bottom-chemisorbed\tTop-chemisorbed\tChemisorbed-mean\t' +
'Bottom-crosslinked\tTop-crosslinked\tCrosslinked-mean')
else:
np.savetxt(output_filename,
np.column_stack((traj.time, S2_bottom, S2_top,
S2_mean_chains, S2_bottom_chemisorbed,
S2_top_chemisorbed, S2_mean_chemisorbed)),
header='Time\tBottom\tTop\tAll-mean\t' +
'Bottom-chemisorbed\tTop-chemisorbed\tChemisorbed-mean')
toss = 1000 / dt # 1 ns
traj = traj[toss:]
# Nematic order parameter
atoms_per_chain = (traj.n_atoms // 2 - 1800 -
hydrogens_per_surface) // n_chains_per_surface
bot_chain_indices = [[
n + x for x in range(atoms_per_chain)
] for n in range(1800, traj.n_atoms // 2 -
hydrogens_per_surface, atoms_per_chain)]
top_chain_indices = [[
n + x for x in range(atoms_per_chain)
] for n in range(traj.n_atoms // 2 + 1800, traj.n_atoms -
hydrogens_per_surface, atoms_per_chain)]
bot_s2 = md.compute_nematic_order(traj, indices=bot_chain_indices)
top_s2 = md.compute_nematic_order(traj, indices=top_chain_indices)
plt.plot(traj.time / 1000, bot_s2, alpha=0.5, lw=0.5, label='bot')
plt.plot(traj.time / 1000, top_s2, alpha=0.5, lw=0.5, label='top')
plt.ylim(0, 1)
plt.xlabel('time (ns)')
plt.ylabel('S2')
plt.legend()
plt.savefig('{}.png'.format(name), bbox_inches='tight')
plt.clf()
S2[name]['top'] = np.mean(top_s2)
S2[name]['top_std'] = np.std(top_s2)
S2[name]['bot'] = np.mean(bot_s2)
