def follow(self, follow_atom = 0, num_neighbours = 3, step_size = 50):
self.num_neighbours = num_neighbours # numbers of nearest neighbours to plot distance to
#try:
# "%d" % follow_atom
#except TypeError:
# print "Generating geometric mean"
# geo = self.traj.get_geometric_center((0,1))
# self.traj.remove_atom(0)
# self.traj.remove_atom(0)
# follow_atom = self.traj.add_atom(geo)
print "Generating symmetric running mean..."
self.pos[:,follow_atom] = symmetric_running_mean(self.pos[:,follow_atom],250)
#pos[:,0] = symmetric_running_mean(pos[:,0],500)
#import profile
#profile.run('get_occupancies()')
# Calculate occupancies, nearest site for atom x and distance to nearest site(s)
self.step_size = step_size
self.occupancies, self.followed_atom_site, self.followed_atom_r2 = self.get_occupancies( follow_atom = follow_atom)
def follow(self, follow_atom=0, num_neighbours=3, step_size=50):
self.num_neighbours = num_neighbours # numbers of nearest neighbours to plot distance to
#try:
# "%d" % follow_atom
#except TypeError:
# print "Generating geometric mean"
# geo = self.traj.get_geometric_center((0,1))
# self.traj.remove_atom(0)
# self.traj.remove_atom(0)
# follow_atom = self.traj.add_atom(geo)
print "Generating symmetric running mean..."
self.pos[:, follow_atom] = symmetric_running_mean(
self.pos[:, follow_atom], 250)
#pos[:,0] = symmetric_running_mean(pos[:,0],500)
#import profile
#profile.run('get_occupancies()')
# Calculate occupancies, nearest site for atom x and distance to nearest site(s)
self.step_size = step_size
self.occupancies, self.followed_atom_site, self.followed_atom_r2 = self.get_occupancies(
follow_atom=follow_atom)
def __init__(self, trajectory, atom = 0, step = 50):
"""
DisplacementPlot class
Parameters:
- trajectory: a oppvasp.Trajectory object
- atom: index of the atom to follow
- step : step size
"""
atpos = trajectory.get_positions( coords = 'cart', atom = atom )
nsteps = atpos.shape[0]
atpos = atpos - atpos[0]
atpos = symmetric_running_mean(atpos,300)
#atpos = symmetric_running_mean(atpos,500)
print " atpos 0: ",atpos[0]
print " atpos -1: ",atpos[-1]
r = np.sqrt(np.sum(atpos**2,axis=1))
self.x = trajectory.time[0:nsteps:step]/1000.
self.y = r[0:nsteps:step]
def __init__(self, trajectory, atom=0, step=50):
"""
DisplacementPlot class
Parameters:
- trajectory: a oppvasp.Trajectory object
- atom: index of the atom to follow
- step : step size
"""
atpos = trajectory.get_positions(coords='cart', atom=atom)
nsteps = atpos.shape[0]
atpos = atpos - atpos[0]
atpos = symmetric_running_mean(atpos, 300)
#atpos = symmetric_running_mean(atpos,500)
print " atpos 0: ", atpos[0]
print " atpos -1: ", atpos[-1]
r = np.sqrt(np.sum(atpos**2, axis=1))
self.x = trajectory.time[0:nsteps:step] / 1000.
self.y = r[0:nsteps:step]
natoms = r.shape[0]
T1[idx] = 1. / 3 * (np.sum(np.cos(fac * r[:, 0])) / natoms +
np.sum(np.cos(fac * r[:, 1])) / natoms +
np.sum(np.cos(fac * r[:, 2])) / natoms)
kvec = np.array((8, 0, 0)) # 2**3 for 2x2x2 supercell
s = 2 * np.pi * np.dot(r, kvec)
#print "T2:",
T2[idx] = np.sum(np.exp(np.complex(0, 1) * s)).real / natoms
cossum = np.sum(np.cos(s)) / natoms
sinsum = np.sum(np.sin(s)) / natoms
T3[idx] = np.sqrt(cossum**2 + sinsum**2)
T1m = symmetric_running_mean(T1, 500)
T2m = symmetric_running_mean(T2, 500)
T3m = symmetric_running_mean(T3, 500)
## for each MD step
#for stepno in range(nsteps):
# x = traj.positions[stepno,:,0] # x coordinate of all atoms
# y = traj.positions[stepno,:,1] # y coordinate of all atoms
# z = traj.positions[stepno,:,2] # z coordinate of all atoms
# lambda_x[stepno] += np.sum(np.cos(fac * x))
# lambda_y[stepno] += np.sum(np.cos(fac * y))
# lambda_z[stepno] += np.sum(np.cos(fac * z))
pbar.finish()
for idx, r in enumerate(pos):
pbar.update(idx)
natoms = r.shape[0]
T1[idx] = 1./3 * (np.sum(np.cos(fac * r[:,0]))/natoms + np.sum(np.cos(fac * r[:,1]))/natoms + np.sum(np.cos(fac * r[:,2]))/natoms )
kvec = np.array((8,0,0)) # 2**3 for 2x2x2 supercell
s = 2*np.pi*np.dot(r, kvec)
#print "T2:",
T2[idx] = np.sum(np.exp(np.complex(0,1) * s)).real / natoms
cossum = np.sum(np.cos(s))/natoms
sinsum = np.sum(np.sin(s))/natoms
T3[idx] = np.sqrt(cossum**2+sinsum**2)
T1m = symmetric_running_mean(T1, 500)
T2m = symmetric_running_mean(T2, 500)
T3m = symmetric_running_mean(T3, 500)
## for each MD step
#for stepno in range(nsteps):
# x = traj.positions[stepno,:,0] # x coordinate of all atoms
# y = traj.positions[stepno,:,1] # y coordinate of all atoms
# z = traj.positions[stepno,:,2] # z coordinate of all atoms
# lambda_x[stepno] += np.sum(np.cos(fac * x))
# lambda_y[stepno] += np.sum(np.cos(fac * y))
# lambda_z[stepno] += np.sum(np.cos(fac * z))
p = [0.15, 0.15, 0.05, 0.05] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
fig = plt.figure()
ax1 = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
ax1.set_xlabel(r'Time [ps]')
ax1.set_ylabel(ur'Si-P bond length [Å]')
ax1.set_ylim(2.0,3.0)
plotdata = []
#dp.ax1.legend(['%d' % (at) for at in include_atoms], loc='upper right', ncol=1, frameon = False)
#dp.ax1.set_ylabel(u'$r^2$ [Å]')
labels = []
lines = []
for i in nearest:
d = dist[:,i]
ds = symmetric_running_mean( d , 500 )
#ax1.plot(traj.time[:], d, color = 'gray', alpha = 0.5)
#line = ax1.plot(traj.time[:]/1.e3, ds )
line = ax1.plot(traj.time[:]/1.e3, d )
lines.append(line[0])
print line
print d.shape
avg = np.sum(d)/d.shape[0]
#ax1.axhline(y=avg, linestyle='dashed')
print i,avg
labels.append('No.%d' % (i))
print u"Shortest Si-P bond length at end of run:",dist[-1,nearest[0]],"Å for Si atom",nearest[0]
si = pos[nearest[0],-1]
p = pos[0,-1]
print "Si:",si
#############################################################################
# (2) Plot
styles = [
{ 'color': 'gray', 'alpha': .5 },
{ 'color': 'black', 'linestyle': '--', 'dashes': (4,1), 'alpha': .8 }, # dashes: (ink on, ink off)
{ 'color': 'black' },
{ 'color': 'gray', 'alpha': .5 } # dashes: (ink on, ink off)
]
plotutils.prepare_canvas( width = '7 cm', fontsize = 9, fontsize_small = 8 )
fig = plt.figure()
toten = traj.total_energy[r_min:r_max]
toten_ra = plotutils.symmetric_running_mean(toten,250)
# Lower plot
p = [0.20, 0.20, 0.05, 0.52] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax0 = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
ax0.plot(traj.time[r_min:r_max]/1.e3, toten, zorder=1, **styles[0])
ax0.plot(traj.time[r_min:r_max]/1.e3, toten_ra, zorder=1, **styles[2])
#ax0.plot(traj.time[r_min:r_max]/1.e3, poten_ra, zorder=1, **styles[1])
ax0.set_xlabel('Time [ps]')
ax0.set_ylabel('$E$ [eV]')
#ax0.set_yticks(np.arange(-340,-300,10))
ymean = np.mean(toten[-500:-1])
ax0.set_ylim(ymean-10, ymean+10)
for line in ax0.yaxis.get_ticklines(): # line is a Line2D instance
line.set_markersize(3)
import psutil
def norm(v):
return np.sqrt(np.dot(v, v))
wdir = './'
basename = os.path.splitext(os.path.basename(sys.argv[0]))[0]
fig_filename = wdir + basename + '.pdf'
traj = read_trajectory(wdir, unwrap_pbcs=True)
poten = (traj.total_energy - traj.kinetic_energy) / traj.num_atoms
t = traj.time / 1.e3 # -> ps
poten_m = symmetric_running_mean(poten, 500)
styles = [{'color': 'black', 'alpha': 0.2}, {'color': 'black', 'alpha': 1.}]
fig = plt.figure()
# Main plot:
p = [0.15, 0.15, 0.05,
0.05] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax1 = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
ax1.grid(True, which='major', color='gray', alpha=0.5)
#ax1.set_ylim((-.2,1.0))
ax1.set_xlabel('Time [ps]')
ax1.set_ylabel('Potential energy per atom [eV]')
ax1.plot(t, poten, **styles[0])
ax1.plot(t, poten_m, **styles[1])
RotatingMarker, ReverseBar, SimpleProgress
import psutil
def norm(v):
return np.sqrt(np.dot(v,v))
wdir = './'
basename = os.path.splitext(os.path.basename(sys.argv[0]))[0]
fig_filename = wdir + basename + '.pdf'
traj = read_trajectory(wdir, unwrap_pbcs = True)
poten = (traj.total_energy - traj.kinetic_energy)/traj.num_atoms
t = traj.time/1.e3 # -> ps
poten_m = symmetric_running_mean(poten, 500)
styles = [
{ 'color': 'black', 'alpha': 0.2 },
{ 'color': 'black', 'alpha': 1. }
]
fig = plt.figure()
# Main plot:
p = [0.15, 0.15, 0.05, 0.05] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax1 = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
ax1.grid(True, which='major', color = 'gray', alpha = 0.5)
#ax1.set_ylim((-.2,1.0))
ax1.set_xlabel('Time [ps]')
ax1.set_ylabel('Potential energy per atom [eV]')
0.05] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
fig = plt.figure()
ax1 = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
ax1.set_xlabel(r'Time [ps]')
ax1.set_ylabel(ur'Si-P bond length [Å]')
ax1.set_ylim(2.0, 3.0)
plotdata = []
#dp.ax1.legend(['%d' % (at) for at in include_atoms], loc='upper right', ncol=1, frameon = False)
#dp.ax1.set_ylabel(u'$r^2$ [Å]')
labels = []
lines = []
for i in nearest:
d = dist[:, i]
ds = symmetric_running_mean(d, 500)
#ax1.plot(traj.time[:], d, color = 'gray', alpha = 0.5)
#line = ax1.plot(traj.time[:]/1.e3, ds )
line = ax1.plot(traj.time[:] / 1.e3, d)
lines.append(line[0])
print line
print d.shape
avg = np.sum(d) / d.shape[0]
#ax1.axhline(y=avg, linestyle='dashed')
print i, avg
labels.append('No.%d' % (i))
print u"Shortest Si-P bond length at end of run:", dist[
-1, nearest[0]], "Å for Si atom", nearest[0]
si = pos[nearest[0], -1]
p = pos[0, -1]
'alpha': .8
}, # dashes: (ink on, ink off)
{
'color': 'black'
},
{
'color': 'gray',
'alpha': .5
} # dashes: (ink on, ink off)
]
plotutils.prepare_canvas(width='7 cm', fontsize=9, fontsize_small=8)
fig = plt.figure()
toten = traj.total_energy[r_min:r_max]
toten_ra = plotutils.symmetric_running_mean(toten, 250)
# Lower plot
p = [0.20, 0.20, 0.05,
0.52] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax0 = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
ax0.plot(traj.time[r_min:r_max] / 1.e3, toten, zorder=1, **styles[0])
ax0.plot(traj.time[r_min:r_max] / 1.e3, toten_ra, zorder=1, **styles[2])
#ax0.plot(traj.time[r_min:r_max]/1.e3, poten_ra, zorder=1, **styles[1])
ax0.set_xlabel('Time [ps]')
ax0.set_ylabel('$E$ [eV]')
#ax0.set_yticks(np.arange(-340,-300,10))
ymean = np.mean(toten[-500:-1])
ax0.set_ylim(ymean - 10, ymean + 10)
for line in ax0.yaxis.get_ticklines(): # line is a Line2D instance
def __init__(self, trajectory, lattice, atom = 0, nn = 8, step = 50, remove_lattice_drift = True, cache = True):
"""
DistanceFromLatticeSites class
Parameters:
- trajectory : oppvasp.Trajectory object
- lattice : oppvasp.Structure object describing the lattice sites
- atom : atom to follow
- nn : number of nearest sites
- step : step size
"""
if trajectory.pbc_unwrapped:
print "----"
print "DistanceFromLatticeSites: Probably best to use a trajectory with PBCs (no unwrapping)"
print "----"
self.entered_site = Event()
self.left_site = Event()
self.bottombar_labels = {}
self.bottombar_height = .5
self.bottombar_colors = {}
self.valid = True
self.lattice = lattice
self.traj = trajectory.get_selection(step = step)
if self.traj.length <= 1:
print "[DistanceFromLatticeSites ERROR] Trajectory selection is too short. Aborting"
self.valid = False
return
ts = self.traj.time[1] - self.traj.time[0]
print "[DistanceFromLatticeSites] Timestamp for trajectory selection is %.f fs" % ts
p = trajectory.path.split('/')
basename = p.pop()
cache_file = '/'.join(p) + '/' + basename.rsplit('.',1)[0] + '_dist_from_lattice.pkl'
print "cache file:",cache_file
if cache and os.path.isfile(cache_file):
pkl_file = open(cache_file, 'rb')
data = pickle.load(pkl_file)
pkl_file.close()
self.x = data['x']
self.y = data['y']
self.sites = data['sites']
print "[DistanceFromLatticeSites] Loaded from cache %s" % cache_file
return
#checks?
if remove_lattice_drift:
print "[DistanceFromLatticeSites] Calculating drift using AvgDistPlotter"
avg = AvgDistPlotter(trajectory = self.traj, lattice = self.lattice, step = 1)
drift = avg.y_smoothed
pos = self.traj.get_positions(coords = 'direct')
print "Min/max: %.2f / %.2f" % (np.min(pos), np.max(pos))
print "[DistanceFromLatticeSites] Unwrapping PBCs before smoothing"
self.traj.unwrap_pbc()
pos = self.traj.get_positions(coords = 'direct')
self.traj.wrap_pbc()
print "Min/max: %.2f / %.2f" % (np.min(pos), np.max(pos))
print "[DistanceFromLatticeSites] Generating symmetric running mean..."
atpos = pos[:,atom]
atpos = symmetric_running_mean(atpos, 250/ts)
atpos = symmetric_running_mean(atpos, 500/ts)
# Wrap back into cell
atpos = atpos - np.floor(atpos)
print "Min/max: %.2f / %.2f" % (np.min(atpos), np.max(atpos))
np.save('temp.npz', atpos)
cell = self.traj.basis
latpos = lattice.get_positions(coords = 'direct')
natoms = pos.shape[1]
nsteps = pos.shape[0]
nsites = latpos.shape[0]
#print "[DistanceFromLatticeSites] Reference lattice contains %d lattice sites" % (nsites)
dists = np.zeros((nsteps, nn), dtype=np.float)
sites = np.zeros((nsteps, nn), dtype=np.int)
pbar = ProgressBar(widgets=['[DistanceFromLatticeSites] Analysing step ',SimpleProgress(),'...'], maxval = nsteps).start()
for i in np.arange(0, nsteps):
pbar.update(i)
# Find displacement vectors between atom and all sites:
r = atpos[i] - latpos
# Use minimum image convention to threat bonds over PBCs
# Note: This will not work with *very* tilted unit cells
r = r - (2*r).astype('int')
#r = r - np.floor(r)
r = direct_to_cartesian(r, cell[i])
if remove_lattice_drift:
r -= drift[i] # cartesian? yes
r2 = (r**2).sum(axis=1)
r = np.sqrt(r2)
idx = np.argsort(r)
dists[i] = r[idx[0:nn]]
sites[i] = idx[0:nn]
#print "Shortest bond is: %.3f Angstrom (between atoms %d and %d)" % (minr,minpair[0],minpair[1])
#break
pbar.finish()
#dists = symmetric_running_median(dists,6)
#dists = symmetric_running_mean(dists,2)
self.x = self.traj.time/1000.
self.y = dists
self.sites = sites
pkl_file = open(cache_file, 'wb')
pickle.dump({'x': self.x, 'y': self.y, 'sites': self.sites, 'trajectory': self.traj.path, 'lattice': lattice }, pkl_file)
pkl_file.close()
print "[DistanceFromLatticeSites] Wrote cache file: %s" % cache_file
