def __init__(self, trajectory_dir = './', lattice_poscar = ''):
self.traj_dir = trajectory_dir
self.lattice_poscar = lattice_poscar
self.traj = read_trajectory( trajectory_dir, unwrap_pbcs = True )
self.pos = self.traj.get_all_trajectories( coords = 'cartesian' )
self.natoms = self.pos.shape[1]
self.nsteps = self.pos.shape[0]
self.poscar = PoscarParser( lattice_poscar )
# Read reference lattice:
self.lattice = self.poscar.get_positions( coords = 'cartesian')
self.nsites = self.lattice.shape[0]
print "Reference POSCAR contains %d lattice sites" % (self.nsites)
self.bottombar_height = .3
self.bottombar_colors = {
892: 'green',
860: 'yellow',
868: 'blue',
844: 'red'
}
def __init__(self, trajectory_dir = './', lattice_poscar = '', atomid = 0):
"""
time_steps in ps
"""
self.traj_dir = trajectory_dir
self.lattice_poscar = lattice_poscar
self.traj = read_trajectory( dir = trajectory_dir, unwrap_pbcs = True )
self.time = self.traj.time/1000.
self.pos = self.traj.get_all_trajectories( coords = 'cart' )
self.natoms = self.pos.shape[1]
self.nsteps = self.pos.shape[0]
#print "Generating symmetric running mean..."
#self.pos[:,atomid] = symmetric_running_mean(self.pos[:,atomid],250)
#pos[:,0] = symmetric_running_mean(pos[:,0],500)
# Read reference lattice:
pp = PoscarParser( lattice_poscar )
self.lattice = pp.get_positions( coords = 'cart')
self.nsites = self.lattice.shape[0]
#print "Reference POSCAR contains %d lattice sites" % (self.nsites)
#import profile
#profile.run('get_occupancies()')
# Calculate occupancies, nearest site for atom x and distance to nearest site(s)
self.nearest_site, self.nearest_site_r = self.find_nearest_sites( atomid = atomid)
def lindemann_index(trajectory_dir='./'):
"""
Calculates the Lindemann index
\delta = \frac{2}{N(N-1)} \sum_{i<j} \frac{\sqrt{\langle r_{ij}^2\rangle_t - \langle r_{ij} \rangle^2_t }}{ \langle r_{ij} \rangle_t }
"""
# Import vasprun.xml:
traj = read_trajectory( dir = trajectory_dir, unwrap_pbcs = False )
pos = traj.get_all_trajectories( coords = 'direct' )
nsteps = pos.shape[0]
natoms = pos.shape[1]
# Get all atom pairs:
pairs = get_pairs(natoms)
npairs = pairs.shape[0]
print "%d steps, %d atoms, %d pairs" % (nsteps, natoms, npairs)
stepsize = 50
samplesteps = np.arange(0,nsteps,stepsize)
r = np.zeros((samplesteps.size, npairs)) # increase stepsize if we run out of memory
r2 = np.zeros((samplesteps.size, npairs)) # increase stepsize if we run out of memory
print "Allocated %.3f MB" % ((r.nbytes+r2.nbytes)/(1024.**2))
pbar = ProgressBar(widgets=['At step...',SimpleProgress()], maxval = samplesteps.size).start()
for idx,s in enumerate(samplesteps):
pbar.update(idx)
x = pos[s,pairs[:,0]] - pos[s,pairs[:,1]]
x = x - (2*x).astype('int') # minimum image convention
r2[idx] = lensq(x)
r[idx] = np.sqrt(r2[idx])
pbar.finish()
meanr2 = np.mean(r2,axis=0)
meanr = np.mean(r,axis=0)
return np.sum(np.sqrt((meanr2 - meanr**2))/meanr)*2/(npairs*2)
def lindemann_index(trajectory_dir="./"):
"""
Calculates the Lindemann index
\delta = \frac{2}{N(N-1)} \sum_{i<j} \frac{\sqrt{\langle r_{ij}^2\rangle_t - \langle r_{ij} \rangle^2_t }}{ \langle r_{ij} \rangle_t }
"""
# Import vasprun.xml:
traj = read_trajectory(dir=trajectory_dir, unwrap_pbcs=False)
pos = traj.get_all_trajectories(coords="direct")
nsteps = pos.shape[0]
natoms = pos.shape[1]
# Get all atom pairs:
pairs = get_pairs(natoms)
npairs = pairs.shape[0]
print "%d steps, %d atoms, %d pairs" % (nsteps, natoms, npairs)
stepsize = 50
samplesteps = np.arange(0, nsteps, stepsize)
r = np.zeros((samplesteps.size, npairs)) # increase stepsize if we run out of memory
r2 = np.zeros((samplesteps.size, npairs)) # increase stepsize if we run out of memory
print "Allocated %.3f MB" % ((r.nbytes + r2.nbytes) / (1024.0 ** 2))
pbar = ProgressBar(widgets=["At step...", SimpleProgress()], maxval=samplesteps.size).start()
for idx, s in enumerate(samplesteps):
pbar.update(idx)
x = pos[s, pairs[:, 0]] - pos[s, pairs[:, 1]]
x = x - (2 * x).astype("int") # minimum image convention
r2[idx] = lensq(x)
r[idx] = np.sqrt(r2[idx])
pbar.finish()
meanr2 = np.mean(r2, axis=0)
meanr = np.mean(r, axis=0)
return np.sum(np.sqrt((meanr2 - meanr ** 2)) / meanr) * 2 / (npairs * 2)
def __init__(self, trajectory_dir='./', lattice_poscar=''):
self.traj_dir = trajectory_dir
self.lattice_poscar = lattice_poscar
self.traj = read_trajectory(trajectory_dir, unwrap_pbcs=True)
self.pos = self.traj.get_all_trajectories(coords='cartesian')
self.natoms = self.pos.shape[1]
self.nsteps = self.pos.shape[0]
self.poscar = PoscarParser(lattice_poscar)
# Read reference lattice:
self.lattice = self.poscar.get_positions(coords='cartesian')
self.nsites = self.lattice.shape[0]
print "Reference POSCAR contains %d lattice sites" % (self.nsites)
self.bottombar_height = .3
self.bottombar_colors = {
892: 'green',
860: 'yellow',
868: 'blue',
844: 'red'
}
#from oppvasp.vasp.parsers import VasprunParser, IterativeVasprunParser, PoscarParser
from progressbar import ProgressBar, Percentage, Bar, ETA, FileTransferSpeed, \
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]')
parser = OptionParser()
pdf_file = os.path.splitext(os.path.basename(sys.argv[0]))[0] + '.pdf'
parser.add_option('--pdf', dest = 'pdf_file', default = pdf_file, help = 'Destination PDF')
parser.add_option('--xml', dest = 'xml_file', default = 'vasprun.xml', help = 'Input vasprun.xml')
parser.add_option('--poscar', dest = 'poscar_file', default = 'POSCAR', help = 'Input POSCAR')
parser.add_option('-f', '--firststep', metavar='STEP', dest = 'first_step', type='int', default = 1, help = 'First ion step to include')
parser.add_option('-l', '--laststep', metavar='STEP', dest = 'last_step', type='int', default = -1, help = 'Last ion step to include')
parser.add_option('-a', '--atom', metavar='ATOM', dest = 'atom_no', type='int', default = 0, help = 'Index of atom to watch (first index is 0)')
(options, args) = parser.parse_args()
print "Range: %i:%i. Atom: %i" % (options.first_step, options.last_step, options.atom_no)
############################## MAIN SCRIPT ######################################
# Read trajectory:
traj = read_trajectory(xml_file = options.xml_file, unwrap_pbcs = True, poscar_file = options.poscar_file)
traj.set_selection(options.first_step, options.last_step)
# Make plot
dp = DisplacementPlot(traj)
dp.add_plot( what = 'r2', smoothen = True, style = { 'linestyle' : '--', 'dashes' : (3,1), 'color' : 'black' } ) # avg r^2 for all atoms
#dp.add_plot( what = 'x', atom_no = 0, smoothen = True, linear_fit = True)
dp.add_plot( what = 'r2', atom_no = 0, smoothen = True, linear_fit = False)
#dp.add_plot( what = 'r', atom_no = 0, smoothen = True, linear_fit = True)
#dp.add_plot( what = 'x', atom_no = 0, smoothen = False, style = { 'zorder' : -1, 'alpha': 0.4, 'color': 'gray' } )
ymax = 0
ymin = 0
for p in dp.plotdata:
ymax_tmp = np.max(p['y'])
if ymax_tmp > ymax:
from oppvasp.vasp.parsers import read_trajectory
#from oppvasp.vasp.parsers import VasprunParser, IterativeVasprunParser, PoscarParser
from progressbar import ProgressBar, Percentage, Bar, ETA, FileTransferSpeed, \
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)
#!/usr/bin/env python
from oppvasp.plotutils import symmetric_running_median, prepare_canvas
prepare_canvas('10 cm')
from oppvasp.vasp.parsers import read_trajectory
import numpy as np
import matplotlib.pyplot as plt
t = read_trajectory()
ekin = t.kinetic_energy/t.num_atoms*1.e3
avgEkin = np.median(ekin)
plt.plot(t.time/1000., ekin, color='blue', alpha=0.2, label = 'Raw data')
plt.plot(t.time/1000., symmetric_running_median(ekin, 250), color='blue', alpha=1, label = '0.25 ps symmetric median')
plt.xlabel("Time [ps]")
plt.ylabel("Kinetic energy per atom [meV]")
plt.axhline(y=avgEkin, color='black', linestyle='--', label = 'Whole run median (%.0f meV)' % avgEkin)
plt.legend(frameon=False,loc='lower right')
plt.savefig('plot_kinetic_energy.pdf')
import os, sys
import numpy as np
from oppvasp.vasp.parsers import read_trajectory
from oppvasp.plotutils import DisplacementPlot
first_step = 0
last_step = -1
if len(sys.argv) > 1:
first_step= int(sys.argv[1])
print "Starting from step %i" % (first_step)
if len(sys.argv) > 2:
last_step = int(sys.argv[2])
print "Ending at step %i" % (last_step)
# Read trajectory:
traj = read_trajectory(unwrap_pbcs = True, poscar_file = 'POSCAR')
traj.set_selection(first_step, last_step)
# Make plot
dp = DisplacementPlot(traj)
dp.add_plot( what = 'D', atom_no = 0, smoothen = True, linear_fit = False)
#dp.ax1.legend(('All atoms', 'P'), ncol = 1, loc = 'upper left', frameon = False)
D = dp.get_diffusion_coeff([0])[0]
td = np.zeros((D.shape[0],2))
td[:,0] = dp.trajs[0].time
td[:,1] = D
np.savetxt('out.csv',td, delimiter='\t')
import os, sys
import numpy as np
from oppvasp.vasp.parsers import read_trajectory
from oppvasp.plotutils import DisplacementPlot
first_step = 0
last_step = -1
if len(sys.argv) > 1:
first_step = int(sys.argv[1])
print "Starting from step %i" % (first_step)
if len(sys.argv) > 2:
last_step = int(sys.argv[2])
print "Ending at step %i" % (last_step)
# Read trajectory:
traj = read_trajectory(unwrap_pbcs=True, poscar_file='POSCAR')
traj.set_selection(first_step, last_step)
# Make plot
dp = DisplacementPlot(traj)
dp.add_plot(what='D', atom_no=0, smoothen=True, linear_fit=False)
#dp.ax1.legend(('All atoms', 'P'), ncol = 1, loc = 'upper left', frameon = False)
D = dp.get_diffusion_coeff([0])[0]
td = np.zeros((D.shape[0], 2))
td[:, 0] = dp.trajs[0].time
td[:, 1] = D
np.savetxt('out.csv', td, delimiter='\t')
Ds = D[-1]
'--atom',
metavar='ATOM',
dest='atom_no',
type='int',
default=0,
help='Index of atom to watch (first index is 0)')
(options, args) = parser.parse_args()
print "Range: %i:%i. Atom: %i" % (options.first_step, options.last_step,
options.atom_no)
############################## MAIN SCRIPT ######################################
# Read trajectory:
traj = read_trajectory(xml_file=options.xml_file,
unwrap_pbcs=True,
poscar_file=options.poscar_file)
traj.set_selection(options.first_step, options.last_step)
# Make plot
dp = DisplacementPlot(traj)
dp.add_plot(what='r2',
smoothen=True,
style={
'linestyle': '--',
'dashes': (3, 1),
'color': 'black'
}) # avg r^2 for all atoms
#dp.add_plot( what = 'x', atom_no = 0, smoothen = True, linear_fit = True)
dp.add_plot(what='r2', atom_no=0, smoothen=True, linear_fit=False)
#dp.add_plot( what = 'r', atom_no = 0, smoothen = True, linear_fit = True)
