def plotAndSaveToFile(self, outFile = 'bandstructure.pdf', silent_overwrite = False, **kwargs):
import matplotlib
matplotlib.use("pdf")
from oppvasp.plotutils import prepare_canvas
from matplotlib import rc
rc('lines', linewidth=0.5)
rc('font',**{'family':'serif','serif':['Palatino']})
rc('font', family='serif')
rc('text', usetex=True)
prepare_canvas('10 cm')
fig = plt.figure()
p = [0.15, 0.15, 0.05, 0.05] # 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] ])
self.plot(ax0, **kwargs)
# Write to file:
if not silent_overwrite and os.path.exists(outFile):
print "\nWARNING: The file \"%s\" already exists." % outFile
if query_yes_no(" Do you want to overwrite it?") == 'no':
return
sys.stdout.write("\nSaving band structure to %s... " % outFile)
sys.stdout.flush()
plt.savefig(outFile)
sys.stdout.write("done!\n\n")
def plotAndSaveToFile(self,
outFile='bandstructure.pdf',
silent_overwrite=False,
**kwargs):
import matplotlib
matplotlib.use("pdf")
from oppvasp.plotutils import prepare_canvas
from matplotlib import rc
rc('lines', linewidth=0.5)
rc('font', **{'family': 'serif', 'serif': ['Palatino']})
rc('font', family='serif')
rc('text', usetex=True)
prepare_canvas('10 cm')
fig = plt.figure()
p = [0.15, 0.15, 0.05, 0.05
] # 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]])
self.plot(ax0, **kwargs)
# Write to file:
if not silent_overwrite and os.path.exists(outFile):
print "\nWARNING: The file \"%s\" already exists." % outFile
if query_yes_no(" Do you want to overwrite it?") == 'no':
return
sys.stdout.write("\nSaving band structure to %s... " % outFile)
sys.stdout.flush()
plt.savefig(outFile)
sys.stdout.write("done!\n\n")
def plot_to_file(self,outfile):
import matplotlib
matplotlib.use('pdf')
from oppvasp.plotutils import prepare_canvas
prepare_canvas('8 cm')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
fig.subplots_adjust(left=0.17, bottom=0.20, right=0.80, top=0.95)
self.plot(ax)
plt.savefig(outfile)
def plot_to_file(self, outfile):
import matplotlib
matplotlib.use('pdf')
from oppvasp.plotutils import prepare_canvas
prepare_canvas('8 cm')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
fig.subplots_adjust(left=0.17, bottom=0.20, right=0.80, top=0.95)
self.plot(ax)
plt.savefig(outfile)
def plot_to_file(self, outfile = '', verbose = False):
if not self.valid:
return
print "[DistanceFromLatticeSites] Plotting..."
import matplotlib
matplotlib.use('pdf')
prepare_canvas('10 cm')
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
self.plot(ax, verbose = verbose)
if outfile == '':
outfile = os.path.splitext(sys.argv[0])[0] + '.pdf'
sys.stdout.write("Writing %s... " % os.path.basename(outfile))
sys.stdout.flush()
plt.savefig(outfile)
sys.stdout.write("done!\n")
'alpha': 0.2
}, {
'color': 'green',
'alpha': 0.2
}, {
'color': 'blue',
'alpha': 0.2
}, {
'color': 'black'
}, {
'color': 'green'
}, {
'color': 'blue'
}]
prepare_canvas('10 cm')
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('$\lambda$')
ax1.plot(traj.time[0:nsteps] / 1.e3, T1, **styles[0])
ax1.plot(traj.time[0:nsteps] / 1.e3, T2, **styles[1])
ax1.plot(traj.time[0:nsteps] / 1.e3, T3, **styles[2])
ax1.plot(traj.time[0:nsteps] / 1.e3, T1m, label='T1', **styles[3])
ax1.plot(traj.time[0:nsteps] / 1.e3, T2m, label='T2', **styles[4])
#lambda_t = 1./3 * (lambda_x + lambda_y + lambda_z)
#print "Initial lattice integrity:",lambda_t[0]
#############################################################################
# (2) Plot
styles = [
{ 'color': 'black', 'alpha': 0.2 },
{ 'color': 'green', 'alpha': 0.2 },
{ 'color': 'blue', 'alpha': 0.2 },
{ 'color': 'black' },
{ 'color': 'green' },
{ 'color': 'blue' }
]
prepare_canvas('10 cm')
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('$\lambda$')
ax1.plot(traj.time[0:nsteps]/1.e3, T1, **styles[0])
ax1.plot(traj.time[0:nsteps]/1.e3, T2, **styles[1])
ax1.plot(traj.time[0:nsteps]/1.e3, T3, **styles[2])
ax1.plot(traj.time[0:nsteps]/1.e3, T1m, label = 'T1', **styles[3])
ax1.plot(traj.time[0:nsteps]/1.e3, T2m, label = 'T2', **styles[4])
ax1.plot(traj.time[0:nsteps]/1.e3, T3m, label = 'T3', **styles[5])
def plot_to_file(self, fig_filename=''):
"""
Automatic plot method. This method is not very robust, and should in general be modified
to highlight the points of interest.
"""
styles = [
{
'color': 'black'
},
{
'color': 'gray',
'alpha': .5
}, # dashes: (ink on, ink off)
{
'color': 'gray',
'linestyle': '--',
'dashes': (6, 2),
'alpha': .5
}, # dashes: (ink on, ink off)
{
'color': 'blue'
}
]
prepare_canvas('10 cm')
fig = plt.figure()
#
#ax1.set_ylabel(u'Distance [Å]')
#ax1.set_xlabel('Time [ps]')
#print self.occ.shape
#print self.occ
#plt.pcolor(C, cmap=mpl.cm.gray_r )
# http://matplotlib.sourceforge.net/examples/pylab_examples/pcolor_demo2.html
#p = [0.15, 0.15, 0.05, 0.52] # 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] ])
#norm = mpl.colors.Normalize(vmin=0, vmax=3)
#im = ax1.imshow(self.occ.T, cmap=mpl.cm.gray_r, norm=norm, aspect='auto', interpolation='nearest')
nsteps = self.occ.shape[0]
nsites = self.occ.shape[1]
natoms = self.traj.num_atoms
lw = 10.
#
p = [0.15, 0.05, 0.05, 0.05
] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax2 = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
#print self.vel.shape
#ax2.acorr(self.vel[:,0,0],usevlines=True, lw=2)
#norm2 = mpl.colors.Normalize(vmin=0, vmax=natoms)
maxocc = 1
imgrid = np.ones(
(nsteps, nsites * maxocc * lw, 3)) # RGB float32 array
colors = np.ones((natoms, 3)) * .9 # default atom color
colors[0] = [1., 0., 0.] # color for atom 0
print self.occ.shape
print self.inhabitants.shape
print imgrid.shape
for i in range(self.occ.shape[0]):
for j in range(self.occ.shape[1]):
#imgrid[i,j*maxocc*lw:j*maxocc*lw+lw] = colors[self.inhabitants[i,j,0]]
for k in range(self.occ[i, j]):
imgrid[i, (j * maxocc + k) * lw:(j * maxocc + k) * lw +
lw] = colors[self.inhabitants[
i, j,
k]] # 0,natoms -> 1,natoms+1 so 0=no atoms
if k + 1 >= maxocc:
print "Warning: Too high occupancy"
break
# #print (j*4+k)/4., colors[self.inhabitants[i,j,k]]
im2 = ax2.imshow(np.transpose(imgrid, axes=[1, 0, 2]),
aspect='auto',
interpolation='nearest')
#im2.
#im2 = ax2.imshow(np.transpose(imgrid, axes=[1,0,2]), aspect='auto', interpolation='nearest')
#ax2.set_ylim(-1*maxocc*lw,nsites*maxocc*lw+maxocc+lw)
#ax2.minorticks_on()
#ax2.set_yticks(np.arange(0,64*maxocc*lw,4), minor=True)
#ax2.set_yticks(np.arange(0,64*4,10*4))
#ax2.set_yticks([])
#ax2.minorticks_off()
#ax1.
#ticks = ax2.get_yticks()
#print ticks
#labels = [ t/4. for t in ticks ]
#print labels
#ax2.set_yticklabels(labels)
# y_max = 3.
# y_sub = .3
# prev_site = -1
# cur_site = -1
# cur_count = 0
# n = self.occupancies.shape[0]
# for i in range(n):
# cur_site = self.followed_atom_site[i,0]
# if cur_site != prev_site:
# if cur_count*self.step_size > 1000:
# ax1.fill([time[i-cur_count], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# ax1.text(time[i-cur_count]+(time[i]-time[i-cur_count])/2.,y_max-(y_sub/2.),'%d' % (prev_site), horizontalalignment='center', verticalalignment='center', fontsize = 8)
# ax1.axvline(time[i], color = 'red', alpha = 0.5)
# cur_count = 0
# prev_site = cur_site
# cur_count += 1
##if label_added:
## ax1.fill([time[i-cur_count+1], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# cur_count-=1
# if cur_count*self.step_size > 1000:
# ax1.fill([time[i-cur_count], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# ax1.text(time[i-cur_count]+(time[i]-time[i-cur_count])/2.,y_max-(y_sub/2.),'%d' % (prev_site), horizontalalignment='center', verticalalignment='center', fontsize = 8)
if fig_filename == '':
fig_filename = os.path.splitext(sys.argv[0])[0] + '.pdf'
sys.stdout.write("Writing %s... " % os.path.basename(fig_filename))
sys.stdout.flush()
plt.savefig(fig_filename)
sys.stdout.write("done!\n")
print
# min/max x:
r_min = 0
r_max = 5000
#############################################################################
# (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])
def plot(self):
"""
Automatic plot method. This method is not very robust, and should in general be modified
to highlight the points of interest.
"""
styles = [
{ 'color': 'black' },
{ 'color': 'gray', 'alpha': .5 }, # dashes: (ink on, ink off)
{ 'color': 'gray', 'linestyle': '--', 'dashes': (6,2), 'alpha': .5 }, # dashes: (ink on, ink off)
{ 'color': 'blue' }
]
prepare_canvas('10 cm')
fig = plt.figure()
p = [0.15, 0.57, 0.05, 0.03] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
upper = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
upper.set_ylabel(u'$r(t)-r(0)$ [Å]')
upper.set_xticklabels([])
p = [0.15, 0.15, 0.05, 0.45] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
lower = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
lower.set_xlabel('Time $t$ [ps]')
lower.set_ylabel(u'$r(t)-R$ (å)')
time = self.traj.time[::self.step_size]/1.e3
y = np.sqrt(self.followed_atom_r2[:,0:self.num_neighbours])
lower.plot(time, y, **styles[0])
ticks = [0.]
ticklabels = [0.]
for i in range(y.shape[1]):
lower.axhline(y[0,i], color = 'black', linestyle='dashed', alpha = 0.5)
print "Y:",y[0,i]
ticks.append(y[0,i])
ticklabels.append("%.2f" % (y[0,i]))
lower.set_yticks(ticks)
lower.set_yticklabels(ticklabels)
r = np.sqrt(np.sum((self.pos[::self.step_size,0]-self.pos[0,0])**2, axis=1))
print time.shape
print r.shape
upper.plot(time, r)
# ================== Paint bottombar ==================================
prev_site = -1
cur_site = -1
cur_count = 0
n = self.occupancies.shape[0]
labelled_sites = []
for i in range(n):
cur_site = self.followed_atom_site[i,0]
if cur_site != prev_site and prev_site != -1:
labelled_sites = self.bottombar_fill(lower, prev_site, time[i-cur_count], time[i], labelled_sites)
cur_count = 0
prev_site = cur_site
cur_count += 1
#cur_count-=1
labelled_sites = self.bottombar_fill(lower, prev_site, time[-cur_count], time[-1], labelled_sites)
self.plt = plt
self.lower = lower
self.upper = upper
def plot_to_file(self, fig_filename = ''):
"""
Automatic plot method. This method is not very robust, and should in general be modified
to highlight the points of interest.
"""
styles = [
{ 'color': 'black' },
{ 'color': 'gray', 'alpha': .5 }, # dashes: (ink on, ink off)
{ 'color': 'gray', 'linestyle': '--', 'dashes': (6,2), 'alpha': .5 }, # dashes: (ink on, ink off)
{ 'color': 'blue' }
]
prepare_canvas('10 cm')
fig = plt.figure()
#
#ax1.set_ylabel(u'Distance [Å]')
#ax1.set_xlabel('Time [ps]')
#print self.occ.shape
#print self.occ
#plt.pcolor(C, cmap=mpl.cm.gray_r )
# http://matplotlib.sourceforge.net/examples/pylab_examples/pcolor_demo2.html
#p = [0.15, 0.15, 0.05, 0.52] # 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] ])
#norm = mpl.colors.Normalize(vmin=0, vmax=3)
#im = ax1.imshow(self.occ.T, cmap=mpl.cm.gray_r, norm=norm, aspect='auto', interpolation='nearest')
nsteps = self.occ.shape[0]
nsites = self.occ.shape[1]
natoms = self.traj.num_atoms
lw = 10.
#
p = [0.15, 0.05, 0.05, 0.05] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
ax2 = fig.add_axes([ p[0], p[1], 1-p[2]-p[0], 1-p[3]-p[1] ])
#print self.vel.shape
#ax2.acorr(self.vel[:,0,0],usevlines=True, lw=2)
#norm2 = mpl.colors.Normalize(vmin=0, vmax=natoms)
maxocc = 1
imgrid = np.ones((nsteps, nsites*maxocc*lw,3)) # RGB float32 array
colors=np.ones((natoms,3))*.9 # default atom color
colors[0] = [1.,0.,0.] # color for atom 0
print self.occ.shape
print self.inhabitants.shape
print imgrid.shape
for i in range(self.occ.shape[0]):
for j in range(self.occ.shape[1]):
#imgrid[i,j*maxocc*lw:j*maxocc*lw+lw] = colors[self.inhabitants[i,j,0]]
for k in range(self.occ[i,j]):
imgrid[i,(j*maxocc+k)*lw:(j*maxocc+k)*lw+lw] = colors[self.inhabitants[i,j,k]] # 0,natoms -> 1,natoms+1 so 0=no atoms
if k+1 >= maxocc:
print "Warning: Too high occupancy"
break
# #print (j*4+k)/4., colors[self.inhabitants[i,j,k]]
im2 = ax2.imshow(np.transpose(imgrid, axes=[1,0,2]), aspect='auto', interpolation='nearest')
#im2.
#im2 = ax2.imshow(np.transpose(imgrid, axes=[1,0,2]), aspect='auto', interpolation='nearest')
#ax2.set_ylim(-1*maxocc*lw,nsites*maxocc*lw+maxocc+lw)
#ax2.minorticks_on()
#ax2.set_yticks(np.arange(0,64*maxocc*lw,4), minor=True)
#ax2.set_yticks(np.arange(0,64*4,10*4))
#ax2.set_yticks([])
#ax2.minorticks_off()
#ax1.
#ticks = ax2.get_yticks()
#print ticks
#labels = [ t/4. for t in ticks ]
#print labels
#ax2.set_yticklabels(labels)
# y_max = 3.
# y_sub = .3
# prev_site = -1
# cur_site = -1
# cur_count = 0
# n = self.occupancies.shape[0]
# for i in range(n):
# cur_site = self.followed_atom_site[i,0]
# if cur_site != prev_site:
# if cur_count*self.step_size > 1000:
# ax1.fill([time[i-cur_count], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# ax1.text(time[i-cur_count]+(time[i]-time[i-cur_count])/2.,y_max-(y_sub/2.),'%d' % (prev_site), horizontalalignment='center', verticalalignment='center', fontsize = 8)
# ax1.axvline(time[i], color = 'red', alpha = 0.5)
# cur_count = 0
# prev_site = cur_site
# cur_count += 1
##if label_added:
## ax1.fill([time[i-cur_count+1], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# cur_count-=1
# if cur_count*self.step_size > 1000:
# ax1.fill([time[i-cur_count], time[i-cur_count], time[i], time[i]], [y_max,y_max-y_sub,y_max-y_sub,y_max], color = 'green', linewidth=0., alpha = 0.2)
# ax1.text(time[i-cur_count]+(time[i]-time[i-cur_count])/2.,y_max-(y_sub/2.),'%d' % (prev_site), horizontalalignment='center', verticalalignment='center', fontsize = 8)
if fig_filename == '':
fig_filename = os.path.splitext(sys.argv[0])[0] + '.pdf'
sys.stdout.write("Writing %s... " % os.path.basename(fig_filename))
sys.stdout.flush()
plt.savefig(fig_filename)
sys.stdout.write("done!\n")
print
def plot(self):
"""
Automatic plot method. This method is not very robust, and should in general be modified
to highlight the points of interest.
"""
styles = [
{
'color': 'black'
},
{
'color': 'gray',
'alpha': .5
}, # dashes: (ink on, ink off)
{
'color': 'gray',
'linestyle': '--',
'dashes': (6, 2),
'alpha': .5
}, # dashes: (ink on, ink off)
{
'color': 'blue'
}
]
prepare_canvas('10 cm')
fig = plt.figure()
p = [0.15, 0.57, 0.05, 0.03
] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
upper = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
upper.set_ylabel(u'$r(t)-r(0)$ [Å]')
upper.set_xticklabels([])
p = [0.15, 0.15, 0.05, 0.45
] # margins: left, bottom, right, top. Height: 1-.15-.46 = .39
lower = fig.add_axes([p[0], p[1], 1 - p[2] - p[0], 1 - p[3] - p[1]])
lower.set_xlabel('Time $t$ [ps]')
lower.set_ylabel(u'$r(t)-R$ (å)')
time = self.traj.time[::self.step_size] / 1.e3
y = np.sqrt(self.followed_atom_r2[:, 0:self.num_neighbours])
lower.plot(time, y, **styles[0])
ticks = [0.]
ticklabels = [0.]
for i in range(y.shape[1]):
lower.axhline(y[0, i],
color='black',
linestyle='dashed',
alpha=0.5)
print "Y:", y[0, i]
ticks.append(y[0, i])
ticklabels.append("%.2f" % (y[0, i]))
lower.set_yticks(ticks)
lower.set_yticklabels(ticklabels)
r = np.sqrt(
np.sum((self.pos[::self.step_size, 0] - self.pos[0, 0])**2,
axis=1))
print time.shape
print r.shape
upper.plot(time, r)
# ================== Paint bottombar ==================================
prev_site = -1
cur_site = -1
cur_count = 0
n = self.occupancies.shape[0]
labelled_sites = []
for i in range(n):
cur_site = self.followed_atom_site[i, 0]
if cur_site != prev_site and prev_site != -1:
labelled_sites = self.bottombar_fill(lower, prev_site,
time[i - cur_count],
time[i], labelled_sites)
cur_count = 0
prev_site = cur_site
cur_count += 1
#cur_count-=1
labelled_sites = self.bottombar_fill(lower, prev_site,
time[-cur_count], time[-1],
labelled_sites)
self.plt = plt
self.lower = lower
self.upper = upper
{
'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))
