def main():
curr_dir = os.getcwd()
all_nums = np.arange(0, 20, dtype=int)
all_simnums = np.arange(0, 5, dtype=int)
all_sweeps = ['sweep{}'.format(num) for num in all_nums]
all_sims = ['Sim{}'.format(num) for num in all_simnums]
all_s2 = []
all_apt = []
for sweep in all_sweeps:
for sim in all_sims:
os.chdir(os.path.join(curr_dir, sweep, sim))
print("Converting in {}".format((sweep, sim)))
trajfile, grofile = prepare_traj(trajname='combined_nopbc.xtc')
traj = mdtraj.load(trajfile, top=grofile)
print("Analyzing in {}".format((sweep, sim)))
s2list = compute_disorder(traj)
aptlist = compute_packing(traj)
all_s2.append(s2list)
all_apt.append(aptlist)
os.chdir(curr_dir)
all_s2 = prune_arrays(all_s2)
fig, ax = plt.subplots(1, 1)
frames = np.arange(all_s2.shape[1])
l, = ax.plot(frames, np.mean(all_s2, axis=0))
ax.fill_between(frames,
np.mean(all_s2, axis=0) - np.std(all_s2, axis=0),
np.mean(all_s2, axis=0) + np.std(all_s2, axis=0),
color=l.get_color(),
alpha=0.4)
ax.set_xlabel("Frame")
ax.set_ylabel("S2")
plot_ay.tidyUp(fig, ax, gridArgs={}, tightLayoutArgs={})
fig.savefig('permeation_order.png')
np.savetxt(
"s2_permeation.dat",
np.column_stack((frames, np.mean(all_s2, axis=0), np.std(all_s2,
axis=0))))
plt.close(fig)
all_apt = prune_arrays(all_apt)
fig, ax = plt.subplots(1, 1)
frames = np.arange(all_apt.shape[1])
l, = ax.plot(frames, np.mean(all_apt, axis=0))
ax.fill_between(frames,
np.mean(all_apt, axis=0) - np.std(all_apt, axis=0),
np.mean(all_apt, axis=0) + np.std(all_apt, axis=0),
color=l.get_color(),
alpha=0.4)
ax.set_xlabel("Frame")
ax.set_ylabel("APT [$\AA^2$]")
plot_ay.tidyUp(fig, ax, gridArgs={}, tightLayoutArgs={})
fig.savefig('permeation_apt.png')
np.savetxt(
"apt_permeation.dat",
np.column_stack((frames, np.mean(all_apt,
axis=0), np.std(all_apt, axis=0))))
plt.close(fig)
def test_grid():
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
x_data = np.arange(0, 10)
linear = (5 * x_data) + 2
quadratic = x_data**2
fig, ax = plt.subplots(1, 1)
ax.plot(x_data, linear, label='linear')
ax.plot(x_data, quadratic, label='quadratic')
gridArgs = {}
gridArgs['color'] = 'r'
gridArgs['linestyle'] = '--'
gridArgs['linewidth'] = 5
ax.set_xlabel("x")
plot_ay.tidyUp(fig, ax, gridArgs=gridArgs)
plt.close(fig)
def test_legend():
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
x_data = np.arange(0, 10)
linear = (5 * x_data) + 2
quadratic = x_data**2
fig, ax = plt.subplots(1, 1)
ax.plot(x_data, linear, label='linear')
ax.plot(x_data, quadratic, label='quadratic')
legendArgs = {}
legendArgs['loc'] = 1
legendArgs['fancybox'] = True
legendArgs['ncol'] = 2
legendArgs['edgecolor'] = 'black'
ax.set_xlabel("x")
plot_ay.tidyUp(fig, ax, legendArgs=legendArgs)
plt.close(fig)
def test_tightlayout():
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
x_data = np.arange(0, 10)
linear = (5 * x_data) + 2
quadratic = x_data**2
fig, ax = plt.subplots(1, 2)
ax[0].plot(x_data, linear, label='linear')
ax[0].plot(x_data, quadratic, label='quadratic')
ax[1].plot(x_data, linear, label='linear')
ax[1].plot(x_data, quadratic, label='quadratic')
tightLayoutArgs = {}
tightLayoutArgs['pad'] = 0.2
tightLayoutArgs['w_pad'] = 10
tightLayoutArgs['h_pad'] = 2
plot_ay.tidyUp(fig, ax[0], tightLayoutArgs=tightLayoutArgs)
plot_ay.tidyUp(fig, ax[1], tightLayoutArgs=tightLayoutArgs)
plt.close(fig)
second_color = '#ff7f0e'
ax2.semilogy(rxn_coordinates, bootstrap_diff_profile, color=second_color)
ax2.fill_between(rxn_coordinates,
bootstrap_diff_profile - bootstrap_diff_err_profile,
bootstrap_diff_profile + bootstrap_diff_err_profile,
alpha=0.4,
color=second_color)
ax2.axhline(y=5.87e-5, color='r', linestyle='--')
ax2.set_ylim(ylim)
for ytick in ax2.get_yticklabels():
ytick.set_color(second_color)
#ax.axvline(x=top_interface, color='k', linestyle=':')
#ax.axvline(x=bot_interface, color='k', linestyle=':')
ax2.set_ylabel(r"Diffusion (cm$^2$/sec)", color=second_color)
plot_ay.tidyUp(fig, ax, tightLayoutArgs={}, gridArgs={})
fig.tight_layout()
fig.savefig('stacked_profiles.png', transparent=True)
plt.close(fig)
fig, ax = plt.subplots(1, 1)
l, = ax.plot(rxn_coordinates, bootstrap_resist_profile)
ax.fill_between(rxn_coordinates,
bootstrap_resist_profile - bootstrap_resist_err_profile,
bootstrap_resist_profile + bootstrap_resist_err_profile,
alpha=0.4,
color=l.get_color())
#ax.axvline(x=top_interface, color='k', linestyle=':')
#ax.axvline(x=bot_interface, color='k', linestyle=':')
ax.set_xlabel(r"Reaction Coordinate (nm)")
ax.set_ylabel("Resistance (sec/cm$^2$)")
oh_bars = ax.bar(oh_x_vals,
ohbar_vals,
bar_width,
yerr=ohyerr_vals,
label='67% OH, 33% DSPC')
ffa_bars = ax.bar(ffa_x_vals,
ffabar_vals,
bar_width,
yerr=ffayerr_vals,
label='67% FFA, 33% DSPC')
ax.set_xticks(xtick_vals)
ax.set_xticklabels(['12', '16', '24'])
ax.set_ylabel("O-H relaxation (ps)")
ax.set_xlabel("Tail length")
plot_ay.tidyUp(fig, ax, legendArgs={'loc': 3}, tightLayoutArgs={})
fig.savefig("oh_relaxation.png")
plt.close(fig)
############################
### H-H vector correlation ###
############################
fig, ax = plt.subplots(1, 1)
ffabar_vals = []
ffayerr_vals = []
ffa_components = ['ffa12', 'ffa16', 'ffa24']
for component in ffa_components:
thing = df.loc[(df[component] == 43.0) & (df['DSPC'] == 21.0)]
ffabar_vals.append(thing['hh_tau_mean'].values[0])
ffayerr_vals.append(thing['hh_tau_std'].values[0])
def analyze_time_aggregation(xtc='npt_nopbc.xtc', gro='npt.gro'):
traj = mdtraj.load('npt_nopbc.xtc', top='npt.gro')
gra_atoms = [
a.index for r in traj.topology.residues if 'GRA_5' in r.name
for a in r.atoms
]
dopc_residues = [r for r in traj.topology.residues if 'DOPC' in r.name]
chol_residues = [r for r in traj.topology.residues if 'CHL1' in r.name]
gra_centers = mdtraj.compute_center_of_mass(traj.atom_slice(gra_atoms))
dopc_centers = np.zeros((traj.n_frames, len(dopc_residues), 3))
chol_centers = np.zeros((traj.n_frames, len(chol_residues), 3))
dopc_distances = np.zeros((traj.n_frames, len(dopc_residues)))
chol_distances = np.zeros((traj.n_frames, len(chol_residues)))
frame_step = 5 # one frame is 5 ps
for i, dopc in enumerate(dopc_residues):
dopc_atoms = [a.index for a in dopc.atoms]
dopc_centers[:, i, :] = mdtraj.compute_center_of_mass(
traj.atom_slice(dopc_atoms))
for i, chol in enumerate(chol_residues):
chol_atoms = [a.index for a in chol.atoms]
chol_centers[:, i, :] = mdtraj.compute_center_of_mass(
traj.atom_slice(chol_atoms))
for i, gra_center in enumerate(gra_centers):
for j in range(len(dopc_residues)):
chol_distances[i, j] = euclidean(gra_center, chol_centers[i, j, :])
for j in range(len(chol_residues)):
dopc_distances[i, j] = euclidean(gra_center, dopc_centers[i, j, :])
fig, ax = plt.subplots(1, 1)
l, = ax.plot(frame_step * np.arange(traj.n_frames),
np.mean(dopc_distances, axis=1),
label='Avg dopc distance')
ax.fill_between(
frame_step * np.arange(traj.n_frames),
np.mean(dopc_distances, axis=1) - np.std(dopc_distances, axis=1),
np.mean(dopc_distances, axis=1) + np.std(dopc_distances, axis=1),
alpha=0.4,
color=l.get_color())
np.savetxt(
'dopc_distances.dat',
np.column_stack((frame_step * np.arange(traj.n_frames),
np.mean(dopc_distances,
axis=1), np.std(dopc_distances, axis=1))))
l, = ax.plot(frame_step * np.arange(traj.n_frames),
np.mean(chol_distances, axis=1),
label='Avg chol distance')
ax.fill_between(
frame_step * np.arange(traj.n_frames),
np.mean(chol_distances, axis=1) - np.std(chol_distances, axis=1),
np.mean(chol_distances, axis=1) + np.std(chol_distances, axis=1),
alpha=0.4,
color=l.get_color())
np.savetxt(
'chol_distances.dat',
np.column_stack((frame_step * np.arange(traj.n_frames),
np.mean(chol_distances,
axis=1), np.std(chol_distances, axis=1))))
ax.set_ylabel("Distance (nm)")
ax.set_xlabel("Time (ps)")
plot_ay.tidyUp(fig, ax, gridArgs={}, legendArgs={}, tightLayoutArgs={})
fig.savefig('Distances.png')
