def s_order(job):
dim = 1
box_range = [0.167, 1.167]
pore_center = (box_range[1]-box_range[0])/2 + box_range[0]
fig, ax = plt.subplots()
s_list = list()
for trj in md.iterload(os.path.join(job.ws, 'nvt.dcd'), top=os.path.join(job.ws, 'init.mol2'), chunk=9000, skip=2001):
water_bonds = get_bond_array(trj)
bins, s_values = compute_s(trj,
dim,
pore_center=pore_center,
bond_array=water_bonds)
s_list.append(s_values)
s_mean = np.mean(s_list, axis=0)
s_std = np.std(s_list, axis=0)
plt.plot(bins, s_mean)
plt.fill_between(bins, s_mean + s_std, s_mean - s_std, alpha=0.2)
plt.xlabel('z-position (nm)')
plt.ylabel('S')
with job:
plt.savefig('s_order.pdf')
np.savetxt('s_order.txt', np.transpose(np.vstack([bins, s_mean, s_std])),
header='Bins\tS_mean\tS_std')
np.savetxt(f'data/{job.sp.nwater}_mol_s_order.txt', np.transpose(np.vstack([bins, s_mean, s_std])),
header='Bins\tS_mean\tS_std')
def s_order(job, symmetrize=False):
dim = 1
box_range = [0.167, 1.167]
pore_center = (box_range[1] - box_range[0]) / 2 + box_range[0]
fig, ax = plt.subplots()
s_list = list()
if job.sp.nwater == 1:
skip = 1
else:
skip = 5001
for trj in md.iterload(
os.path.join(job.ws, "nvt.trr"),
top=os.path.join(job.ws, "init.mol2"),
chunk=5000,
skip=skip,
):
water_bonds = get_bond_array(trj)
bins, s_values = compute_s(
trj,
dim,
pore_center=pore_center,
bond_array=water_bonds,
symmetrize=symmetrize,
)
s_list.append(s_values)
s_mean = np.mean(s_list, axis=0)
s_std = np.std(s_list, axis=0)
plt.plot(bins, s_mean)
plt.fill_between(bins, s_mean + s_std, s_mean - s_std, alpha=0.2)
plt.xlabel("z-position (nm)")
plt.ylabel("S")
if symmetrize == True:
extension = "_symmetrize"
else:
extension = ""
with job:
plt.savefig(f"s_order{extension}.pdf")
np.savetxt(
f"s_order{extension}.txt",
np.transpose(np.vstack([bins, s_mean, s_std])),
header="Bins\tS_mean\tS_std",
)
np.savetxt(
f"data/{job.sp.nwater}_mol_s_order{extension}.txt",
np.transpose(np.vstack([bins, s_mean, s_std])),
header="Bins\tS_mean\tS_std",
)
def s_order(job):
dim = 1
box_range = [0.5, 1.5]
pore_center = (box_range[1] - box_range[0]) / 2 + box_range[0]
fig, ax = plt.subplots()
s_list = list()
trj = md.load(os.path.join(job.ws, "carbon_water-pos-1.pdb"))
trj.save(os.path.join(job.ws, "carbon_water-pos-1.xyz"),
force_overwrite=True)
for trj in md.iterload(
os.path.join(job.ws, "carbon_water-pos-1.xyz"),
top=os.path.join(job.ws, "init.mol2"),
chunk=5000,
skip=6000,
):
trj = md.Trajectory(
trj.xyz,
trj.top,
unitcell_lengths=np.tile([0.9824, 2.0000, 1.0635],
(trj.n_frames, 1)),
unitcell_angles=np.tile([90.0, 90.0, 90.0], (trj.n_frames, 1)),
)
bins, s_values = compute_s(trj, dim, pore_center=pore_center)
s_list.append(s_values)
s_mean = np.mean(s_list, axis=0)
s_std = np.std(s_list, axis=0)
s_stdem = stats.sem(s_list, axis=0)
plt.plot(bins, s_mean)
plt.fill_between(bins, s_mean + s_std, s_mean - s_std, alpha=0.2)
plt.xlabel("z-position (nm)")
plt.ylabel("S")
with job:
plt.savefig(
project.root_directory() +
"/data/{}/s_order.pdf".format(str(job.sp.nwater) + "water_data"))
np.savetxt(
project.root_directory() +
"/data/{}/s_order.txt".format(str(job.sp.nwater) + "water_data"),
np.transpose(np.vstack([bins, s_mean, s_stdem, s_std])),
header="Bins\tS_mean\tS_stdem\tS_std",
)
def s_order(job):
dim = 1
box_range = [0.837, 2.837]
pore_center = (box_range[1] - box_range[0]) / 2 + box_range[0]
fig, ax = plt.subplots()
s_list = list()
for trj in md.iterload(
os.path.join(job.ws, "nvt.trr"),
top=os.path.join(job.ws, "init.mol2"),
chunk=5000,
skip=5001,
):
water_bonds = get_bond_array(trj)
bins, s_values = compute_s(trj,
dim,
pore_center=pore_center,
bond_array=water_bonds)
s_list.append(s_values)
s_mean = np.mean(s_list, axis=0)
s_std = np.std(s_list, axis=0)
plt.plot(bins, s_mean)
plt.fill_between(bins, s_mean + s_std, s_mean - s_std, alpha=0.2)
plt.xlabel("z-position (nm)")
plt.ylabel("S")
plt.ylim((-0.45, 0.45))
with job:
plt.savefig("s_order.pdf")
np.savetxt(
"s_order.txt",
np.transpose(np.vstack([bins, s_mean, s_std])),
header="Bins\tS_mean\tS_std",
)
np.savetxt(
f"data/{job.sp.nwater}_mol_s_order.txt",
np.transpose(np.vstack([bins, s_mean, s_std])),
header="Bins\tS_mean\tS_std",
)
def main():
project = signac.get_project("../")
for nwater, group in project.groupby("nwater"):
# New dataframe to save the results
df = pd.DataFrame()
# Create pore system
pore_width = 1.0 * u.nm
filled_pore = mbuild.recipes.GraphenePoreSolvent(
pore_length=1.0,
pore_depth=1.1,
pore_width=pore_width.to_value("nm"),
n_sheets=1,
slit_pore_dim=2,
x_bulk=0,
solvent=spce_water,
n_solvent=nwater,
)
# Translate to centered at 0,0,0 and make box larger in z
box_center = filled_pore.periodicity / 2.0
filled_pore.translate(-box_center)
filled_pore.periodicity[2] = 2.0
xy_area = filled_pore.periodicity[0] * filled_pore.periodicity[1]
top = filled_pore.to_trajectory(residues=["RES", "SOL"])
# Load all trajectories and combine
for job in group:
run = job.sp.run
# Load in full trajectory
full_traj = md.load(job.fn("prod.nvt.out.xyz"), top=top)
# Add unit cell information
full_traj = md.Trajectory(
full_traj.xyz,
full_traj.top,
unitcell_lengths=np.tile(filled_pore.periodicity,
(full_traj.n_frames, 1)),
unitcell_angles=np.tile([90., 90., 90.],
(full_traj.n_frames, 1)),
)
# Keep only water
slice_water = full_traj.top.select("water and name O H")
traj = full_traj.atom_slice(slice_water)
slice_ow = traj.top.select("name O")
slice_hw = traj.top.select("name H")
traj_ow = traj.atom_slice(slice_ow)
traj_hw = traj.atom_slice(slice_hw)
# Compute the density
bin_centers_ow, density_ow = compute_density(traj_ow,
xy_area,
bin_width=0.005)
bin_centers_hw, density_hw = compute_density(traj_hw,
xy_area,
bin_width=0.005)
# Compute the s order parameter
bin_centers_s, s_results = compute_s(traj, bin_width=0.005)
assert np.allclose(bin_centers_ow, bin_centers_hw)
assert np.allclose(bin_centers_ow, bin_centers_s)
# Save results
tmp_df = pd.DataFrame()
tmp_df[f"run"] = run
tmp_df[f"z-loc_nm"] = bin_centers_ow
tmp_df[f"density-ow_nm^-3"] = density_ow
tmp_df[f"density-hw_nm^-3"] = density_hw
tmp_df[f"s_value"] = s_results
df = df.append(tmp_df)
# Compute mean/stdev and save to file
means = df.groupby("z-loc_nm").mean().drop(
columns=["run"]).add_suffix("_mean")
stds = df.groupby("z-loc_nm").std().drop(
columns=["run"]).add_suffix("_std")
combined = means.merge(stds, on="z-loc_nm")
combined.to_csv(f"results_{nwater}-water.csv")