def plot_smooth():
df = pd.read_csv("VDOS.txt", sep=r"[ \t]", engine="python")
freq, ave = getDos(df['Freq_THz'], df['vdos_av'])
dp = pd.DataFrame()
dp['Freq_THz'] = freq
dp["vdos_av"] = ave
dp.to_csv('smooth_dos.txt', sep='\t', index=False, float_format="%f")
plot((freq, 'Frequency(THz)'), (ave, 'Phonon Density of states'),
"smooth_dos.png",
grid=True)
alldos = [df['vdos_x'], df['vdos_y'], df['vdos_z']]
filter = getFilter(alldos)
dp = pd.DataFrame()
freq, ave = getDos(df['Freq_THz'], df['vdos_x'], filter)
dp['Freq_x'] = freq
dp["vdos_x"] = ave
freq, ave = getDos(df['Freq_THz'], df['vdos_y'], filter)
dp['Freq_y'] = freq
dp["vdos_y"] = ave
freq, ave = getDos(df['Freq_THz'], df['vdos_z'], filter)
dp['Freq_z'] = freq
dp["vdos_z"] = ave
dp.to_csv('smooth_dosxyz.txt', sep='\t', index=False, float_format="%f")
series(xlabel='Frequency(THz)',
ylabel='Phonon Density of States',
datas=[(dp['Freq_x'], dp["vdos_x"], "dos_x"),
(dp['Freq_y'], dp["vdos_y"], "dos_y"),
(dp['Freq_z'], dp["vdos_z"], "dos_z")],
filename='smooth_dosxyz.png',
grid=True)
def sca(self,th=0.0):
qpoints_full=np.loadtxt('BTE.qpoints_full')
ks=qpoints_full[:,2:4]
f=self.direction(ks,th)
ids=qpoints_full[:,1].astype(np.int)[f]
qpoints=np.loadtxt('BTE.qpoints')
idx=qpoints[:,0].astype(np.int)
u=[list(idx).index(i) for i in ids]
w=np.loadtxt('BTE.w_final')
omega=np.loadtxt('BTE.omega')/(2.0*np.pi)
#w[omega<omega.flatten().max()*0.005]=float('nan')
tao=1.0/w+1e-6
rt=tao[u,:3]
rom=omega[u,:3]
data=[]
n,m=rom.shape
for i in range(m):
data.append([rom[:,i],rt[:,i],'b'])
series(xlabel='Frequency (THz)',
ylabel='Relaxation Time (ps)',
datas=data,
filename='scaling-%f.png'%th,scatter=True,legend=False,logx=True,logy=True)
def sca(self, th=0.0):
qpoints_full = np.loadtxt('BTE.qpoints_full')
ks = qpoints_full[:, 2:4]
f = filter_along_direction(ks, th, eps=0.5)
ids = qpoints_full[:, 1].astype(np.int)[f]
qpoints = np.loadtxt('BTE.qpoints')
idx = qpoints[:, 0].astype(np.int)
u = [list(idx).index(i) for i in ids]
w = np.loadtxt('BTE.w_final')
omega = np.loadtxt('BTE.omega') / (2.0 * np.pi)
# w[omega<omega.flatten().max()*0.005]=float('nan')
tao = 1.0 / w + 1e-6
rt = tao[u, :3]
rom = omega[u, :3]
data = []
n, m = rom.shape
for i in range(m):
data.append([rom[:, i], rt[:, i], 'b'])
series(
xlabel='Frequency (THz)',
ylabel='Relaxation Time (ps)',
datas=data,
filename='scaling-%f.png' % th,
scatter=True,
legend=False,
logx=True,
logy=True)
def plot_regiondos(regions, filename='region_dos.png'):
import h5py
db = h5py.File('velocity.hdf5')
freq = db['/freq']
datas = []
alldos = []
for region, rname in regions:
dos = get_regiondos(region)
alldos.append(dos)
filter = getFilter(alldos)
dp = pd.DataFrame()
for region, rname in regions:
dos = get_regiondos(region)
x, y = getDos(freq, dos, filter)
datas.append((x, y, "region:%s" % rname))
dp['freq_%s' % rname] = x
dp['dos_%s' % rname] = y
dp.to_csv('region_dos.txt', sep='\t', index=False, float_format="%f")
series(xlabel='Frequency (THz)',
ylabel='Phonon Density of States',
datas=datas,
linewidth=1,
filename=filename,
legend=True)
def getTempProfile(self, begin, upP, deta, S, tcfactor, zfactor):
fas = fixAveSpace('tempProfile.txt')
quants = fas.getConvergence(upP + 1, begin)
tools.to_txt(['Temperature(K)', 'Jx'], quants, 'convergenceT.txt')
snapStep = fas.snapStep
dto = dict(log="step\tkappa\n", plots=[])
coord, aveN, aveQuants = fas.iterate(begin, self.kappaConverge,
snapStep, upP, deta, S, tcfactor,
zfactor, dto)
tools.write(dto['log'], 'convergenceK.txt')
series(
'x(Augstrom)',
'temperature(K)',
dto['plots'],
'convergenceT.png',
linewidth=1,
legend=False)
filter = aveN[:] > 1 # at least 1 atom in the bin
aveC = coord[filter]
aveN = aveN[filter]
aveTemp = aveQuants[filter, 0]
avejx = aveQuants[filter, 1]
nbin = len(avejx)
data = np.c_[np.arange(nbin) + 1, aveC, aveN, aveTemp, avejx]
tools.to_txt(['id', 'Coord', 'Count', 'Temp', 'Jx'], data,
'tempAve.txt')
drawTempAve()
return (aveC, aveN, aveTemp, avejx)
def drawDos(self):
freq,pdos=self.getpdos()
datas=[(freq,p,'') for p in pdos.T]
series('Frequency (THz)','Partial Density of States',
datas=datas,
filename='partial_dos.png',legend=False,grid=True)
plot((freq,'Frequency (THz)'),(np.sum(pdos,axis=1),'Density of States'),filename='total_dos.png')
def plot_dos():
df = pd.read_csv("VDOS.txt", sep=r"[ \t]", engine="python")
freq, dosx, dosy, dosz = df['Freq_THz'], df['vdos_x'], df['vdos_y'], df[
'vdos_z']
series(xlabel='Frequency(THz)',
ylabel='Phonon Density of States',
datas=[(freq, dosx, "dos_x"), (freq, dosy, "dos_y"),
(freq, dosz, "dos_z")],
linewidth=0.3,
filename='VDOS.png')
def modefit(self, iqp, ibr, k, freq, q):
totalStep = self.totalStep
x = np.linspace(0, 1, totalStep / 2 + 1) * 1 / 2.0 / self.timestep
q1 = q
x1 = x
df = 1.0 / (2.0 * self.timestep) / (self.totalStep / 2)
span = int(1.5 / df)
ori = q.argmax() # int(freq/df)
low = max(ori - 1 * span, 0)
hi = min(ori + 1 * span, len(q) - 1)
filter = range(low, hi)
x = x[filter]
q = q[filter] / 1e10
# q=self.lowess(x,q)
# q=self.smo(q,3)
p0 = np.array([x[q.argmax()], 0.01, q[q.argmax()]]) # Initial guess
p = self.fitLife(x, q, p0)
p = np.abs(p)
# to_txt(['Freq','dos'],np.c_[x,q],'SED/single%s%s.txt'%(str(k),freq))
if self.partsed:
xv = x # np.linspace(x.min(),x.max(),1000)
series(
xlabel='Frequency (THz)', ylabel='Single Phonon Power Spectrum', datas=[
(x, q, "origin"), (xv, self.lorentz(
p, xv), "fitting")], linewidth=1, filename='NMA/nmapart_%s_%s_%s_%s.png' %
(iqp, ibr, str(k), freq))
if self.totalsed:
xv = np.linspace(x.min(), x.max(), 100)
series(
xlabel='Frequency (THz)',
ylabel='Single Phonon Power Spectrum',
datas=[
(x1,
q1 /
1e10,
"origin"),
(xv,
self.lorentz(
p,
xv),
"fitting")],
linewidth=1,
filename='NMA/nma_%s_%s_%s_%s.png' %
(iqp,
ibr,
str(k),
freq))
v = map(str, list(k) + [freq, p[0], p[1], 1.0 / p[1]])
c = '\t'.join(v)
print("[nma]", c)
return c, q1
def plot_vacf():
df = pd.read_csv("VACF.txt", sep=r"[ \t]", engine="python")
time, vx, vy, vz = df['correlation_time(ps)'], df['vcaf_x'], df[
'vcaf_y'], df['vcaf_z']
xx = select(time)
time = time[xx]
vx, vy, vz = vx[xx], vy[xx], vz[xx]
series(xlabel='Correlation Time (ps)',
ylabel='Normalized Velocity Auto Correlation Function',
datas=[(time, vx, "vcf_x"), (time, vy, "vcf_y"),
(time, vz, "vcf_z")],
linewidth=0.3,
filename='VACF.png')
def plot_atomdos(atoms=range(24), filename='atom_dos.png'):
import h5py
db = h5py.File('velocity.hdf5')
freq = db['/freq']
datas = []
for i in atoms:
dos = db['/dos_atom/%d' % i]
x, y = getDos(freq, dos)
datas.append((x, y, "dos_atom_%d" % i))
series(xlabel='Frequency (THz)',
ylabel='Phonon Density of States',
datas=datas,
linewidth=0.3,
filename=filename,
legend=False)
def drawDos(self):
freq, pdos = self.getpdos()
datas = [(freq, p, '') for p in pdos.T]
series(
'Frequency (THz)',
'Partial Density of States',
datas=datas,
filename='partial_dos.png',
legend=False,
grid=True)
plot(
(freq, 'Frequency (THz)'), (np.sum(pdos, axis=1),
'Density of States'),
filename='total_dos.png')
def sca2(self):
w=np.loadtxt('BTE.w_final')
omega=np.loadtxt('BTE.omega')/(2.0*np.pi)
w[omega<omega.flatten().max()*0.005]=float('nan')
tao=1.0/w+1e-6
rt=tao[50:55,:]
rom=omega[50:55,:]
data=[]
n,m=rom.shape
for i in range(n):
data.append([rom[i,:],rt[i,:],'b'])
series(xlabel='Frequency (THz)',
ylabel='Relaxation Time (ps)',
datas=data,
filename='k.png',scatter=True,legend=False,logx=True,logy=True)
def reduce(self, n=1, name0=500000):
name = 'ac' + str(name0) + '.dat'
m = self.m
xs = []
for i in range(n):
dir = '../%d/' % i
print dir
x = np.loadtxt(dir + name)
xs.append(x[:, 1])
ts = x[:, 0]
xs = np.array(xs)
avexs = xs.mean(axis=0)
np.savetxt('aveac.txt', np.c_[ts, avexs])
plot([ts, 'Correlation Time (ps)'],
[avexs, 'Heat Flut Correlation Function'], 'aveac.png')
datas = []
for i in range(n):
datas.append([ts, xs[i], 'b'])
series(xlabel='Correlation Time (ps)',
ylabel='Heat Flut Correlation Function',
datas=datas,
filename='ac.png',
legend=False)
name = 'tc' + str(name0) + '.dat'
xs = []
for i in range(n):
dir = '../%d/' % i
print dir
x = np.loadtxt(dir + name)
xs.append(x[:, 1])
ts = x[:, 0]
xs = np.array(xs)
avexs = xs.mean(axis=0)
np.savetxt('avetc.txt', np.c_[ts, avexs])
plot([ts, 'Correlation Time (ps)'],
[avexs, 'Thermal Conductivity (W/mK)'], 'avetc.png')
datas = []
for i in range(n):
datas.append([ts, xs[i], 'b'])
series(xlabel='Correlation Time (ps)',
ylabel='Thermal Conductivity (W/mK)',
datas=datas,
filename='tc.png',
legend=False)
def modefit(self, iqp, ibr, k, freq, q):
totalStep = self.totalStep
x = np.linspace(0, 1, totalStep / 2 + 1) * 1 / 2.0 / self.timestep
q1 = q
x1 = x
df = 1.0 / (2.0 * self.timestep) / (self.totalStep / 2)
span = int(1.5 / df)
ori = q.argmax() #int(freq/df)
low = max(ori - 1 * span, 0)
hi = min(ori + 1 * span, len(q) - 1)
filter = range(low, hi)
x = x[filter]
q = q[filter] / 1e10
#q=self.lowess(x,q)
#q=self.smo(q,3)
p0 = np.array([x[q.argmax()], 0.01, q[q.argmax()]]) #Initial guess
p = self.fitLife(x, q, p0)
p = np.abs(p)
#to_txt(['Freq','dos'],np.c_[x,q],'SED/single%s%s.txt'%(str(k),freq))
if self.partsed:
xv = x #np.linspace(x.min(),x.max(),1000)
series(xlabel='Frequency (THz)',
ylabel='Single Phonon Power Spectrum',
datas=[(x, q, "origin"), (xv, self.lorentz(p,
xv), "fitting")],
linewidth=1,
filename='NMA/nmapart_%s_%s_%s_%s.png' %
(iqp, ibr, str(k), freq))
if self.totalsed:
xv = np.linspace(x.min(), x.max(), 100)
series(xlabel='Frequency (THz)',
ylabel='Single Phonon Power Spectrum',
datas=[(x1, q1 / 1e10, "origin"),
(xv, self.lorentz(p, xv), "fitting")],
linewidth=1,
filename='NMA/nma_%s_%s_%s_%s.png' %
(iqp, ibr, str(k), freq))
v = map(str, list(k) + [freq, p[0], p[1], 1.0 / p[1]])
c = '\t'.join(v)
print "[nma]", c
return c, q1
def calculateSED(self, k, natom_unitcell):
totalStep = self.totalStep
phase = self.getPhase(k, natom_unitcell)
# phase1=self.getPhase(-np.array(k),natom_unitcell)
nd = 1
d = totalStep - nd + 1
phi = np.zeros(d)
# print phase
for j in range(natom_unitcell):
q = np.zeros([totalStep, 3], dtype=np.complex)
# q1=np.zeros([totalStep,3],dtype=np.complex)
for i in range(j, self.natom, natom_unitcell):
fv = self.velocity_atom(i)
q += np.array(fv) * phase[i]
# q+=np.array(fv)*phase1[i]
for k0 in range(nd):
u0 = k0 + d
# if u0>len(q):
# u0=len(q)
u1 = u0 - d
q0 = fft(q[u1:u0], axis=0)
# q0=q0[:totalStep/2+1]
phi += (q0 * q0.conjugate()).real.sum(axis=1)
phi = phi[:d / 2 + 1] / nd
# df = 1.0 / (2.0 * self.timestep) / (self.totalStep / 2)
phi = self.smo(phi, 50)
if self.totalsed:
x = np.linspace(0, 1, d / 2 + 1) * 1 / 2.0 / self.timestep
series(
xlabel='Frequency (THz)',
ylabel='Phonon Energy Spectrum',
datas=[
(x,
phi,
"origin")],
linewidth=1,
filename='NMA/sed%s.png' %
(str(k)),
logy=True)
return phi
def draw_branch_scatter(self):
try:
w = get_w_final('..')
q = get_qpoints('..')
qnorm = np.linalg.norm(q, axis=1)
data = []
n, m = w.shape
for i in range(m):
data.append([qnorm, w[:, i], 'b'])
series(
xlabel='|q| (1/nm)',
ylabel='Scatter Rate (THz)',
datas=data,
filename='branchscatter.png',
scatter=True,
legend=False,
logx=True,
logy=True)
except Exception as e:
pass
def calculateSED(self, k, natom_unitcell):
totalStep = self.totalStep
phase = self.getPhase(k, natom_unitcell)
phase1 = self.getPhase(-np.array(k), natom_unitcell)
nd = 1
d = totalStep - nd + 1
phi = np.zeros(d)
#print phase
for j in range(natom_unitcell):
q = np.zeros([totalStep, 3], dtype=np.complex)
#q1=np.zeros([totalStep,3],dtype=np.complex)
for i in range(j, self.natom, natom_unitcell):
fv = self.velocity_atom(i)
q += np.array(fv) * phase[i]
#q+=np.array(fv)*phase1[i]
for k0 in range(nd):
u0 = k0 + d
#if u0>len(q):
# u0=len(q)
u1 = u0 - d
q0 = fft(q[u1:u0], axis=0)
#q0=q0[:totalStep/2+1]
phi += (q0 * q0.conjugate()).real.sum(axis=1)
phi = phi[:d / 2 + 1] / nd
s = np.zeros_like(phi)
df = 1.0 / (2.0 * self.timestep) / (self.totalStep / 2)
span = int(1.0 / df)
phi = self.smo(phi, 50)
if self.totalsed:
x = np.linspace(0, 1, d / 2 + 1) * 1 / 2.0 / self.timestep
series(xlabel='Frequency (THz)',
ylabel='Phonon Energy Spectrum',
datas=[(x, phi, "origin")],
linewidth=1,
filename='NMA/sed%s.png' % (str(k)),
logy=True)
return phi
def sca2(self):
w = np.loadtxt('BTE.w_final')
omega = np.loadtxt('BTE.omega') / (2.0 * np.pi)
w[omega < omega.flatten().max() * 0.005] = float('nan')
tao = 1.0 / w + 1e-6
rt = tao[50:55, :]
rom = omega[50:55, :]
data = []
n, m = rom.shape
for i in range(n):
data.append([rom[i, :], rt[i, :], 'b'])
series(
xlabel='Frequency (THz)',
ylabel='Relaxation Time (ps)',
datas=data,
filename='k.png',
scatter=True,
legend=False,
logx=True,
logy=True)
import pandas as pd
df = pd.read_csv("region_dos.txt", sep=r"[ \t]", engine="python")
npair = len(df.columns) / 2
datas = []
for i in range(npair):
rname = df.columns[i * 2][5:]
datas.append((df['freq_' + rname], df['dos_' + rname], "region:" + rname))
dc = pd.read_csv("graphenedos.txt", sep=r"[ \t]", engine="python")
datas.append((dc[dc.columns[0]], dc[dc.columns[1]], 'GNR'))
from aces.graph import series
series(xlabel='Frequency (THz)',
ylabel='Phonon Density of States',
datas=datas,
linewidth=1,
filename='camparedos.png',
legend=True,
xmax=60)
def cal_lc(self, r, dr, m):
from aces.lammpsdata import lammpsdata
atoms = lammpsdata().set_src('correlation_structure')
atoms.set_pbc([m.xp, m.yp, m.zp])
nw = self.totalStep / 2 + 1
nr = int(r / dr) + 1
n = len(atoms)
dis = atoms.get_all_distances(mic=True)
pos = atoms.positions
cell = atoms.cell
symbols = atoms.get_chemical_symbols()
masses = self.getMassFromLabel(symbols)
if not exists('g.npz'):
g = np.zeros([nw, nr], dtype=np.complex)
ng = np.zeros(nr, dtype=np.int)
c = self.get_nei(r, dr, pos, cell)
for i, j, q in c:
if j == 0:
print "center atom:%d/%d" % (i, n)
aij = np.einsum('ij,ij->i',
self.fourier_atom(i).conjugate(),
self.fourier_atom(j))
aii = np.einsum('ij,ij->i',
self.fourier_atom(i).conjugate(),
self.fourier_atom(i))
ajj = np.einsum('ij,ij->i',
self.fourier_atom(j).conjugate(),
self.fourier_atom(j))
x = aij / np.sqrt(aii * ajj) * np.sqrt(masses[i] * masses[j])
for ir in q:
g[:, ir] += x
ng[ir] += 1
np.savez('g.npz', g=g, ng=ng)
npz = np.load('g.npz')
g = npz['g']
ng = npz['ng']
rs = (np.arange(nr)) * dr
rs = rs[:-1]
def aa(x):
if x > 0: return 1.0 / x
else: return 0.0
ivr = [aa(x) for x in ng] #1.0/4.0/np.pi/rs**2
g = np.einsum('ij,j->ij', g, ivr)[:, :-1]
g = (g.conjugate() * g).real
imshow(g, 'g.png', extent=[0, 1, 0, 1])
data = []
for i in range(0, self.totalStep / 2 + 1, self.totalStep / 20):
data.append([rs, g[i, :], str(i)])
series(xlabel='length (A)',
ylabel='Cross Phonon Energy Spectrum',
datas=data,
linewidth=2,
filename='cpes.png')
cg = g.cumsum(axis=1)
imshow(cg, 'cg.png', extent=[0, 1, 0, 1])
cg = np.einsum('ij,i->ij', cg, 1.0 / cg[:, -1])
data = []
for i in range(0, self.totalStep / 2 + 1, self.totalStep / 10):
data.append([rs, cg[i, :], str(i)])
series(xlabel='length (A)',
ylabel='Cross Phonon Energy Spectrum',
datas=data,
linewidth=2,
filename='cges.png')
lc = ((cg < 0.80).sum(axis=1)) * dr
x = np.linspace(0, 1, self.totalStep / 2 + 1) * 1 / 2.0 / self.timestep
plot([x, 'Frequency (THz)'], [lc, 'Coherence length(A)'],
'coherence.png')
return lc
import numpy as np
data = []
kapitza = []
i = -1
for dT in range(0, 60, 10):
i += 1
p = np.loadtxt(str(i) + '/tempAve.txt', skiprows=1)
data.append([p[:, 1], p[:, 3], 'dT=' + str(dT)])
dataT = p[30, 3] - p[20, 3]
j = (p[:, 4] * p[:, 2]).sum() / p[:, 2].sum()
kapitza.append([dT, np.abs(j) / dataT])
kapitza = np.array(kapitza)
from aces.graph import series, plot, fig, pl
series(xlabel='x (Angstrom)',
ylabel='Temperature (K)',
datas=data,
filename='profile.png')
with fig('kapitza.png'):
x = kapitza[:, 0]
y = kapitza[:, 1]
pl.plot(x,
y,
marker='v',
ms=12,
mec='b',
mfc='w',
mfcalt="w",
mew=1.5,
linewidth=1)
pl.xlabel('dT(K)')
pl.ylabel('Kapitza Conductance (W/m2K)')
def postold(self):
try:
df = pd.read_csv(
"BTE.kappa_scalar",
sep=r"[ \t]+",
header=None,
names=['step', 'kappa'],
engine='python')
ks = np.array(df['kappa'])
plot(
(np.array(df['step']), 'Iteration Step'),
(ks, 'Thermal Conductivity (W/mK)'),
'kappa_scalar.png',
grid=True,
linewidth=2)
except Exception as e:
print(e)
try:
df = pd.read_csv(
"BTE.cumulative_kappa_scalar",
sep=r"[ \t]+",
header=None,
names=['l', 'kappa'],
engine='python')
ks = np.array(df['kappa'])
plot(
(np.array(df['l']),
'Cutoff Mean Free Path for Phonons (Angstrom)'),
(ks, 'Thermal Conductivity (W/mK)'),
'cumulative_kappa_scalar.png',
grid=True,
linewidth=2,
logx=True)
except Exception as e:
print(e)
try:
omega = np.loadtxt('BTE.omega') / (2.0 * np.pi)
kappa = np.loadtxt('BTE.kappa')[-1, 1:]
kappa = np.einsum('jji', kappa.reshape([3, 3, -1])) / 3.0
plot(
(np.arange(len(omega[0])), 'Band'),
(kappa, 'Thermal Conductivity (W/mK)'),
'kappa_band.png',
grid=True,
linewidth=2)
plot(
(np.arange(len(omega[0])), 'Band'),
(kappa.cumsum(), 'Thermal Conductivity (W/mK)'),
'cumulative_kappa_band.png',
grid=True,
linewidth=2)
except Exception as e:
print(e)
try:
w = np.loadtxt('BTE.w_final')
w = np.abs(w)
w[omega < omega.flatten().max() * 0.005] = float('nan')
plot(
(omega.flatten(), 'Frequency (THz)'), (w.flatten(),
'Scatter Rate (THz)'),
'scatter_freq.png',
grid=True,
scatter=True,
logy=True)
tao = 1.0 / w + 1e-6
with fig('tao_freq.png'):
pl.semilogy(
omega.flatten(),
tao.flatten(),
linestyle='.',
marker='.',
color='r',
markersize=5)
pl.xlabel('Frequency (THz)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
pl.xlim([0, omega.max()])
# pl.ylim([0,tao.flatten().max()])
to_txt(['freq', 'tao'],
np.c_[omega.flatten(), tao.flatten()], 'tao_freq.txt')
except Exception as e:
print(e)
"""
if not exists('relaxtime'):mkdir('relaxtime')
cd('relaxtime')
for i,om in enumerate(omega[:6]):
print "q : ",i
plot((om,'Frequency (THz)'),(tao[i],'Relaxation Time (ps)'),
'tao_freq_q%d.png'%i,grid=True,scatter=True,logx=True,logy=True)
cd('..')
"""
try:
v = np.loadtxt(open('BTE.v'))
n, m = v.shape
v = v.reshape([n, 3, m / 3])
v = np.linalg.norm(v, axis=1)
y = (v.flatten(), 'Group Velocity (nm/ps)')
plot(
(omega.flatten(), 'Frequency (THz)'),
y,
'v_freq.png',
grid=True,
scatter=True)
to_txt(['freq', 'vg'],
np.c_[omega.flatten(), v.flatten()], 'v_freq.txt')
except Exception as e:
print(e)
try:
l = v * tao
l[l < 1e-6] = None
plot(
(omega.flatten(), 'Frequency (THz)'), (l.flatten(),
'Mean Free Path (nm)'),
'lamda_freq.png',
grid=True,
scatter=True,
logy=True,
logx=True,
xmin=0)
to_txt(['freq', 'mfp'],
np.c_[omega.flatten(), l.flatten()], 'lamda_freq.txt')
except Exception as e:
print(e)
try:
q = np.loadtxt(open('BTE.qpoints'))
qnorm = np.linalg.norm(q[:, -3:], axis=1)
data = []
n, m = w.shape
for i in range(m):
data.append([qnorm, w[:, i], 'b'])
series(
xlabel='|q| (1/nm)',
ylabel='Scatter Rate (THz)',
datas=data,
filename='branchscatter.png',
scatter=True,
legend=False,
logx=True,
logy=True)
except Exception as e:
print(e)
def postnew(self):
cd('T300K')
try:
df=pd.read_csv("BTE.kappa_scalar",sep=r"[ \t]+",header=None,names=['step','kappa'],engine='python');
ks=np.array(df['kappa'])
plot((np.array(df['step']),'Iteration Step'),(ks,'Thermal Conductivity (W/mK)'),'kappa_scalar.png',grid=True,linewidth=2)
except Exception as e:
print e
try:
df=pd.read_csv("BTE.cumulative_kappa_scalar",sep=r"[ \t]+",header=None,names=['l','kappa'],engine='python');
ks=np.array(df['kappa'])
plot((np.array(df['l']),'Cutoff Mean Free Path for Phonons (Angstrom)'),(ks,'Thermal Conductivity (W/mK)'),'cumulative_kappa_scalar.png',grid=True,linewidth=2,logx=True)
except Exception as e:
print e
try:
omega=np.loadtxt('../BTE.omega')/(2.0*np.pi)
kappa=np.loadtxt('BTE.kappa')[-1,1:]
kappa=np.einsum('jji',kappa.reshape([3,3,-1]))/3.0
plot((np.arange(len(omega[0])),'Band'),(kappa,'Thermal Conductivity (W/mK)'),'kappa_band.png',grid=True,linewidth=2)
plot((np.arange(len(omega[0])),'Band'),(kappa.cumsum(),'Thermal Conductivity (W/mK)'),'cumulative_kappa_band.png',grid=True,linewidth=2)
except Exception as e:
print e
try:
w=np.loadtxt('BTE.w_final')[:,1]
w=np.abs(w)
q=np.loadtxt(open('../BTE.qpoints'))
n=len(q)
w=w.T.reshape([-1,n])
w=np.einsum('jk->kj',w)
w.flags.writeable = True
print w.shape,omega.shape
w[omega<omega.flatten().max()*0.005]=float('nan')
plot((omega.flatten(),'Frequency (THz)'),(w.flatten(),'Scatter Rate (THz)'),'scatter_freq.png',grid=True,scatter=True,logy=True)
tao=1.0/w+1e-6
plot((omega.flatten(),'Frequency (THz)'),(tao.flatten(),'Relaxation Time (ps)'),'tao_freq.png',grid=True,scatter=True,logy=True)
to_txt(['freq','tao'],np.c_[omega.flatten(),tao.flatten()],'tao_freq.txt')
except Exception as e:
print e
try:
v=np.loadtxt(open('../BTE.v'))
q=np.loadtxt(open('../BTE.qpoints'))
n=len(q)
v=v.T.reshape([3,-1,n])
v=np.einsum('ijk->kji',v)
v=np.linalg.norm(v,axis=-1)
plot((omega.flatten(),'Frequency (THz)'),(v.flatten(),'Group Velocity (nm/ps)'),'v_freq.png',grid=True,scatter=True)
to_txt(['freq','vg'],np.c_[omega.flatten(),v.flatten()],'v_freq.txt')
l=v*tao
plot((omega.flatten(),'Frequency (THz)'),(l.flatten(),'Mean Free Path (nm)'),'lamda_freq.png',grid=True,scatter=True)
to_txt(['freq','mfp'],np.c_[omega.flatten(),l.flatten()],'lamda_freq.txt')
except Exception as e:
print e
try:
g=np.loadtxt('../BTE.gruneisen')
plot((omega.flatten(),'Frequency (THz)'),(g.flatten(),'Gruneisen'),'gruneisen_freq.png',grid=True,scatter=True)
with fig('gruneisen_freq.png'):
pl.scatter(omega.flatten(),g.flatten(),marker='.',color='r',s =50)
pl.xlabel('Frequency (THz)')
pl.ylabel('Gruneisen Coeffecient')
#pl.grid(True)
pl.xlim([0,omega.max()])
pl.ylim([-10,5])
#pl.tick_params(axis='both', which='major', labelsize=14)
to_txt(['freq','gruneisen'],np.c_[omega.flatten(),g.flatten()],'gruneisen_freq.txt')
g=np.loadtxt('../BTE.P3')
with fig('p3_freq.png'):
pl.scatter(omega.flatten(),g.flatten()*1e6,marker='.',color='r',s =50)
pl.xlabel('Frequency (THz)')
pl.ylabel('P3 $(\\times 10^{-6})$')
#pl.grid(True)
pl.xlim([0,omega.max()])
pl.ylim([0,g.max()*1e6])
to_txt(['freq','p3'],np.c_[omega.flatten(),g.flatten()],'p3_freq.txt')
except Exception as e:
print e
try:
q=np.loadtxt(open('../BTE.qpoints'))
qnorm=np.linalg.norm(q[:,-3:],axis=1)
data=[]
n,m=w.shape
for i in range(m):
data.append([qnorm,w[:,i],'b'])
series(xlabel='|q| (1/nm)',
ylabel='Scatter Rate (THz)',
datas=data,
filename='branchscatter.png',scatter=True,legend=False,logx=True,logy=True)
except Exception as e:
pass
cd('..')
def postsheng(self):
try:
df=pd.read_csv("BTE.kappa_scalar",sep=r"[ \t]+",header=None,names=['step','kappa'],engine='python');
ks=np.array(df['kappa'])
plot((np.array(df['step']),'Iteration Step'),(ks,'Thermal Conductivity (W/mK)'),'kappa_scalar.png',grid=True,linewidth=2)
except Exception as e:
print e
try:
df=pd.read_csv("BTE.cumulative_kappa_scalar",sep=r"[ \t]+",header=None,names=['l','kappa'],engine='python');
ks=np.array(df['kappa'])
plot((np.array(df['l']),'Cutoff Mean Free Path for Phonons (Angstrom)'),(ks,'Thermal Conductivity (W/mK)'),'cumulative_kappa_scalar.png',grid=True,linewidth=2,logx=True)
except Exception as e:
print e
try:
omega=np.loadtxt('BTE.omega')/(2.0*np.pi)
kappa=np.loadtxt('BTE.kappa')[-1,1:]
kappa=np.einsum('jji',kappa.reshape([3,3,-1]))/3.0
plot((np.arange(len(omega[0])),'Band'),(kappa,'Thermal Conductivity (W/mK)'),'kappa_band.png',grid=True,linewidth=2)
plot((np.arange(len(omega[0])),'Band'),(kappa.cumsum(),'Thermal Conductivity (W/mK)'),'cumulative_kappa_band.png',grid=True,linewidth=2)
except Exception as e:
print e
try:
w=np.loadtxt('BTE.w_final')
w=np.abs(w)
w[omega<omega.flatten().max()*0.005]=float('nan')
plot((omega.flatten(),'Frequency (THz)'),(w.flatten(),'Scatter Rate (THz)'),'scatter_freq.png',grid=True,scatter=True,logy=True)
tao=1.0/w+1e-6
with fig('tao_freq.png'):
pl.semilogy(omega.flatten(),tao.flatten(),linestyle='.',marker='.',color='r',markersize =5)
pl.xlabel('Frequency (THz)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
pl.xlim([0,omega.max()])
#pl.ylim([0,tao.flatten().max()])
to_txt(['freq','tao'],np.c_[omega.flatten(),tao.flatten()],'tao_freq.txt')
except Exception as e:
print e
"""
if not exists('relaxtime'):mkdir('relaxtime')
cd('relaxtime')
for i,om in enumerate(omega[:6]):
print "q : ",i
plot((om,'Frequency (THz)'),(tao[i],'Relaxation Time (ps)'),'tao_freq_q%d.png'%i,grid=True,scatter=True,logx=True,logy=True)
cd('..')
"""
try:
v=np.loadtxt(open('BTE.v'))
n,m=v.shape
v=v.reshape([n,3,m/3])
v=np.linalg.norm(v,axis=1)
plot((omega.flatten(),'Frequency (THz)'),(v.flatten(),'Group Velocity (nm/ps)'),'v_freq.png',grid=True,scatter=True)
to_txt(['freq','vg'],np.c_[omega.flatten(),v.flatten()],'v_freq.txt')
except Exception as e:
print e
try:
l=v*tao
l[l<1e-6]=None
plot((omega.flatten(),'Frequency (THz)'),(l.flatten(),'Mean Free Path (nm)'),'lamda_freq.png',grid=True,scatter=True,logy=True,logx=True,xmin=0)
to_txt(['freq','mfp'],np.c_[omega.flatten(),l.flatten()],'lamda_freq.txt')
except Exception as e:
print e
try:
q=np.loadtxt(open('BTE.qpoints'))
qnorm=np.linalg.norm(q[:,-3:],axis=1)
data=[]
n,m=w.shape
for i in range(m):
data.append([qnorm,w[:,i],'b'])
series(xlabel='|q| (1/nm)',
ylabel='Scatter Rate (THz)',
datas=data,
filename='branchscatter.png',scatter=True,legend=False,logx=True,logy=True)
except Exception as e:
print e
import pandas as pd
df=pd.read_csv("region_dos.txt",sep=r"[ \t]",engine="python");
npair=len(df.columns)/2
datas=[]
for i in range(npair):
rname=df.columns[i*2][5:]
datas.append((df['freq_'+rname],df['dos_'+rname],"region:"+rname))
dc=pd.read_csv("graphenedos.txt",sep=r"[ \t]",engine="python");
datas.append((dc[dc.columns[0]],dc[dc.columns[1]],'GNR'))
from aces.graph import series
series(xlabel='Frequency (THz)',
ylabel='Phonon Density of States',
datas=datas
,linewidth=1
,filename='camparedos.png',legend=True,xmax=60)
datas=[]
import pandas as pd
import numpy as np
from aces.tools import *
youngs=[]
temps=range(200,300,10)
for i in range(10):
print i
cd(str(i))
from aces.App import App
App().runner.post()
df=pd.read_csv("cal_stress.txt",sep=r"[ \t]",engine="python");
datas.append([df['Strain'],df['Stress_GPa'],str(temps[i])+'K' ])
youngs.append(float(read("YoungsModulus.txt")))
cd('..')
from aces.graph import series,plot
plot((np.array(temps),'Temperature (K)'),(np.array(youngs),'Young\'s Modulus (GPa)'),'youngs.png')
series(xlabel='Strain',
ylabel='Stress (GPa)',
datas=datas
,linewidth=1
,filename='stress_strain.png',legend=True)
import pandas as pd
import numpy as np
from aces.tools import *
youngs = []
temps = np.arange(100, 300, 20)
sel = np.arange(0, 10, 2)
for i in sel:
print i
cd(str(i * 2 + 1))
#from aces.App import App
#App().runner.post()
df = pd.read_csv("cal_stress.txt", sep=r"[ \t]", engine="python")
datas.append([df['Strain'], df['Stress_GPa'], str(temps[i]) + 'K'])
cd('..')
for i in np.arange(0, 10):
print i
cd(str(i * 2))
youngs.append(float(read("YoungsModulus.txt")))
cd('..')
from aces.graph import series, plot
series(xlabel='Strain',
ylabel='Stress (GPa)',
datas=datas,
linewidth=1,
filename='stress_strain.png',
legend=True,
grid=True)
plot((np.array(temps), 'Temperature (K)'),
(np.array(youngs), 'Young\'s Modulus (GPa)'), 'youngs.png')
def cal_lc(self, r, dr, m):
from aces.lammpsdata import lammpsdata
atoms = lammpsdata().set_src('correlation_structure')
atoms.set_pbc([m.xp, m.yp, m.zp])
nw = self.totalStep / 2 + 1
nr = int(r / dr) + 1
n = len(atoms)
pos = atoms.positions
cell = atoms.cell
symbols = atoms.get_chemical_symbols()
masses = self.getMassFromLabel(symbols)
if not tl.exists('g.npz'):
g = np.zeros([nw, nr], dtype=np.complex)
ng = np.zeros(nr, dtype=np.int)
c = self.get_nei(r, dr, pos, cell)
for i, j, q in c:
if j == 0:
print("center atom:%d/%d" % (i, n))
aij = np.einsum(
'ij,ij->i',
self.fourier_atom(i).conjugate(),
self.fourier_atom(j))
aii = np.einsum(
'ij,ij->i',
self.fourier_atom(i).conjugate(),
self.fourier_atom(i))
ajj = np.einsum(
'ij,ij->i',
self.fourier_atom(j).conjugate(),
self.fourier_atom(j))
x = aij / np.sqrt(aii * ajj) * np.sqrt(masses[i] * masses[j])
for ir in q:
g[:, ir] += x
ng[ir] += 1
np.savez('g.npz', g=g, ng=ng)
npz = np.load('g.npz')
g = npz['g']
ng = npz['ng']
rs = (np.arange(nr)) * dr
rs = rs[:-1]
def aa(x):
if x > 0:
return 1.0 / x
else:
return 0.0
ivr = [aa(x) for x in ng] # 1.0/4.0/np.pi/rs**2
g = np.einsum('ij,j->ij', g, ivr)[:, :-1]
g = (g.conjugate() * g).real
imshow(g, 'g.png', extent=[0, 1, 0, 1])
data = []
for i in range(0, self.totalStep / 2 + 1, self.totalStep / 20):
data.append([rs, g[i, :], str(i)])
series(xlabel='length (A)',
ylabel='Cross Phonon Energy Spectrum',
datas=data, linewidth=2, filename='cpes.png')
cg = g.cumsum(axis=1)
imshow(cg, 'cg.png', extent=[0, 1, 0, 1])
cg = np.einsum('ij,i->ij', cg, 1.0 / cg[:, -1])
data = []
for i in range(0, self.totalStep / 2 + 1, self.totalStep / 10):
data.append([rs, cg[i, :], str(i)])
series(xlabel='length (A)',
ylabel='Cross Phonon Energy Spectrum',
datas=data, linewidth=2, filename='cges.png')
lc = ((cg < 0.80).sum(axis=1)) * dr
x = np.linspace(0, 1, self.totalStep / 2 + 1) * 1 / 2.0 / self.timestep
plot([x, 'Frequency (THz)'], [lc, 'Coherence length(A)'], 'coherence.png')
return lc
