def plot_hist(entry_hist):
'''
Plot the number of users for different numbers of entries.
Ignore large numbers of entries where the number of users is only 1.
'''
num_entries = []
num_users = []
for n in entry_hist:
num_entries.append(n['_id'])
num_users.append(n['nusers'])
num_entries = np.array(num_entries)
num_users = np.array(num_users)
percent_users_less = 100.0*np.cumsum(num_users)/np.sum(num_users)
ind = np.where(num_users > 1)[0][-1]
plt.plot(num_entries[0:ind+1], percent_users_less[0:ind+1], '.-')
mpsa.axis_setup('x')
mpsa.axis_setup('y')
plt.xlabel(r'Number of entries', labelpad=mpsa.axeslabelpad)
plt.ylabel(r'\% of users with $\le$ number of entries',
labelpad=mpsa.axeslabelpad)
plt.title('Percentage of users with a given number of entries or fewer.\n\
Numbers of entries are only shown for the largest number\n\
of entries associated with 2 or more users.')
mpsa.save_figure('entry_count.png')
plt.close()
print tabulate(np.column_stack((num_entries[0:21], num_users[0:21],
percent_users_less[0:21])),
headers=['# Entries', '# Users',
'% Users with # Entries or Fewer'])
def plot_pmfs_same_dnmp(infile_prefix, infile_extension, nuc_name,
outfile_prefix, sys_list):
nsysnames = len(sys_list)
for isysname in range(nsysnames):
sysname = sys_list[isysname]
infile = infile_prefix + '_' + nuc_name + '_' + sysname + '.' + infile_extension
data = read_xvg(infile)
kbT = data[:, 1]
ax = plt.subplot(111)
if sysname == 'ch3':
surf = data[:, 0] - 1.83 + 0.388
plt.plot(surf, kbT, 'g', label='CH3-SAM')
with open('pmf_table_ch3.dat', 'ab') as f:
pmf_min = np.min(kbT)
pmf_min_idx = np.argmin(kbT[:-10])
surf_idx = int(np.argwhere(surf >= 0)[0])
surf_idx_end = int(np.argwhere(surf >= 0.8)[0])
Fads = integrate.simps(kbT[surf_idx:surf_idx_end],
surf[surf_idx:surf_idx_end])
dw_max = surf[pmf_min_idx:surf_idx_end][np.argmax(
kbT[pmf_min_idx:surf_idx_end])]
table = np.column_stack((pmf_min, Fads, dw_max))
np.savetxt(f,
table,
header="pmf_min|Fads|dw_max||--->" + nuc_name)
elif sysname == 'cooch3':
surf = data[:, 0] - 2.05 + 0.388
plt.plot(surf, data[:, 1], 'b', label='COOCH3-SAM')
with open('pmf_table_cooch3.dat', 'ab') as f:
pmf_min = np.min(kbT)
pmf_min_idx = np.argmin(kbT[:-10])
surf_idx = int(np.argwhere(surf >= 0)[0])
surf_idx_end = int(np.argwhere(surf >= 0.8)[0])
Fads = integrate.simps(kbT[surf_idx:surf_idx_end],
surf[surf_idx:surf_idx_end])
dw_max = surf[pmf_min_idx:surf_idx_end][np.argmax(
kbT[pmf_min_idx:surf_idx_end])]
table = np.column_stack((pmf_min, Fads, dw_max))
np.savetxt(f,
table,
header="pmf_min|Fads|dw_max||--->" + nuc_name)
else:
surf = data[:, 0] - 2.18 + 0.388
plt.plot(surf, data[:, 1], 'r', label='OC6H5-SAM')
with open('pmf_table_poxy.dat', 'ab') as f:
pmf_min = np.min(kbT)
pmf_min_idx = np.argmin(kbT[:-10])
surf_idx = int(np.argwhere(surf >= 0)[0])
surf_idx_end = int(np.argwhere(surf >= 0.8)[0])
Fads = integrate.simps(kbT[surf_idx:surf_idx_end],
surf[surf_idx:surf_idx_end])
dw_max = surf[pmf_min_idx:surf_idx_end][np.argmax(
kbT[pmf_min_idx:surf_idx_end])]
table = np.column_stack((pmf_min, Fads, dw_max))
np.savetxt(f,
table,
header="pmf_min|Fads|dw_max||--->" + nuc_name)
plt.xlim(0.0, 0.8)
plt.ylim(-8, 10)
#mpsa.axis_setup('x')
# mpsa.axis_setup('y')
plt.xlabel("$d_w$ (nm)")
plt.ylabel('PMF ($ k_B$T)')
plt.legend()
xmajorLocator = MultipleLocator(0.2)
xminorLocator = MultipleLocator(0.05)
ymajorLocator = MultipleLocator(4)
yminorLocator = MultipleLocator(1)
ax.xaxis.set_major_locator(xmajorLocator)
ax.xaxis.set_minor_locator(xminorLocator)
ax.yaxis.set_major_locator(ymajorLocator)
ax.yaxis.set_minor_locator(yminorLocator)
# plt.rcParams['xtick.labelsize']=12
# plt.rcParams['ytick.labelsize']=12
mpsa.save_figure(outfile_prefix + '_' + nuc_name + '.png')
xvg_files = args.xvg_files
# P atom coodinates on each leaflet
P_upper = read_xvg(xvg_files[0])
P_lower = read_xvg(xvg_files[1])
bilayer_thickness = np.column_stack(
(P_upper[:, 0], P_upper[:, 1] - P_lower[:, 1]))
np.savetxt('thickness_' + sys_name + '.xvg', bilayer_thickness)
#bilayer_thickness_filtered= savgol_filter(bilayer_thickness[:,1], 101, 2)
Temp = np.linspace(int(T_start), int(T_end), num=len(P_upper))
plt.plot(Temp, bilayer_thickness)
plt.xlim(250, 360)
plt.ylim(3.3, 3.6)
mpsa.axis_setup('x')
mpsa.axis_setup('y')
plt.legend()
plt.xlabel('T (K)', labelpad=mpsa.axeslabelpad)
plt.ylabel('Bilayer Thickness (nm)', labelpad=mpsa.axeslabelpad)
mpsa.save_figure('thickness_' + sys_name + '.png')
plt.show()
def cmpr_noneq_eq(infile_extension, sysname, nuc_list, outfile_prefix):
nnucs = len(nuc_list)
for inuc in range(nnucs):
nuc = nuc_list[inuc]
infile = 'pmf_shift' + '_' + nuc + '_' + sysname + '.' + infile_extension
infile2 = nuc + '_zcoord_to_prob.dat'
nonequil_zcoord = np.loadtxt(infile2)
shift_pmf = np.loadtxt(infile)
energy = shift_pmf[:, 1]
dw = shift_pmf[:, 0] - dsurf + r_probe
Q = np.sum(np.exp(-energy))
ads_prob = np.exp(-energy) / Q
density = ads_prob / (dw[20] - dw[19])
# print(len(ads_prob))
ax = plt.subplot(111)
if nuc == 'dCMP':
#plt.plot(dw,ads_prob,'g',label='equil')
plt.plot(dw, density, 'g', label='equil')
plt.plot(nonequil_zcoord[:, 0],
nonequil_zcoord[:, 1],
'k',
label='non-equil')
if sysname == 'cooch3':
smooth = np.loadtxt('dCMP_zcoord_to_prob_smoothcooch3.dat')
plt.plot(smooth[:, 0], smooth[:, 1], 'c--', label='smooth')
elif nuc == 'dTMP':
#plt.plot(dw,ads_prob,'b',label='equil')
plt.plot(dw, density, 'b', label='equil')
plt.plot(nonequil_zcoord[:, 0],
nonequil_zcoord[:, 1],
'k',
label='non-equil')
if sysname == 'cooch3':
smooth = np.loadtxt('dTMP_zcoord_to_prob_smoothcooch3.dat')
plt.plot(smooth[:, 0], smooth[:, 1], 'c--', label='smooth')
elif nuc == 'dGMP':
#plt.plot(dw,ads_prob,'m',label='equil')
plt.plot(dw, density, 'm', label='equil')
plt.plot(nonequil_zcoord[:, 0],
nonequil_zcoord[:, 1],
'k',
label='non-equil')
if sysname == 'cooch3':
smooth = np.loadtxt('dGMP_zcoord_to_prob_smoothcooch3.dat')
plt.plot(smooth[:, 0], smooth[:, 1], 'c--', label='smooth')
else:
#plt.plot(dw,ads_prob,'r',label='equil')
plt.plot(dw, density, 'r', label='equil')
plt.plot(nonequil_zcoord[:, 0],
nonequil_zcoord[:, 1],
'k',
label='non-equil')
if sysname == 'cooch3':
smooth = np.loadtxt('dAMP_zcoord_to_prob_smoothcooch3.dat')
plt.plot(smooth[:, 0], smooth[:, 1], 'c--', label='smooth')
plt.xlim(0.0, 0.6)
if sysname == 'ch3':
plt.ylim(0, 12)
ymajor = 4
yminor = 1
elif sysname == 'cooch3':
plt.ylim(0, 2.8)
ymajor = 0.5
yminor = 0.1
else:
plt.ylim(0, 18)
ymajor = 3
yminor = 1
xmajorLocator = MultipleLocator(0.2)
xminorLocator = MultipleLocator(0.05)
ymajorLocator = MultipleLocator(ymajor)
yminorLocator = MultipleLocator(yminor)
ax.xaxis.set_major_locator(xmajorLocator)
ax.xaxis.set_minor_locator(xminorLocator)
ax.yaxis.set_major_locator(ymajorLocator)
ax.yaxis.set_minor_locator(yminorLocator)
plt.xlabel("$d_w $(nm)")
plt.ylabel('Pd ($nm^{-1}$)')
plt.legend()
mpsa.save_figure(nuc + '_' + outfile_prefix + '_' + sysname + '.png')
plt.close()
kbT = read_xvg(profile)[:195, 1]
wids = read_xvg(profile)[:195, 0]
j = -1
for i in wids >= 3.4:
if i == False:
j += 1
ave = np.mean(kbT[j:])
kbT[:] = kbT[:] - ave
data = np.column_stack((wids, kbT))
np.savetxt('pmf_shift_' + outfile_prefix + '.dat', data)
#sbstct=substract(traj1,traj2)
#ave_dist=np.mean(sbstct[100:,1])
#np.savetxt(dnmp+'_edgrp_SH_'+'dist'+ '.dat',ave_dist.reshape(1,))
plt.plot(wids, kbT, 'r', linewidth=2)
plt.axhline(y=0, ls='--')
plt.xlim(0, 4.2)
plt.ylim(-25, 10)
mpsa.axis_setup('x')
mpsa.axis_setup('y')
plt.xlabel("$d_w$ (nm)", labelpad=mpsa.axeslabelpad)
#plt.tick_params(labelsize=15)
plt.ylabel('PMF ($k_B$T)', labelpad=mpsa.axeslabelpad)
mpsa.save_figure(outfile_prefix + '.png')
# plt.show()