def calculateDos(self):
totalVcf = np.zeros([self.totalStep, 4])
for i in range(self.natom):
print "atom", i
vcf = self.correlate_atom(i)
totalVcf[:, :3] += vcf
totalVcf[:, 3] = np.average(totalVcf[:, :3], axis=1)
#normalization
vcf0 = totalVcf[0].copy()
for i, vcf in enumerate(totalVcf):
totalVcf[i] /= vcf0
totalStep = self.totalStep
data = np.c_[self.times, totalVcf]
to_txt(
['correlation_time(ps)', 'vcaf_x', 'vcaf_y', 'vcaf_z', 'vcaf_av'],
data[:totalStep / 2], 'VACF.txt')
totalDos = np.abs(rfft(totalVcf, axis=0))[:totalStep / 2]
data = np.c_[self.freq, totalDos]
to_txt(['Freq_THz', 'vdos_x', 'vdos_y', 'vdos_z', 'vdos_av'], data,
'VDOS.txt')
print 'VACF and VDOS caculated OK'
plot_dos()
plot_vacf()
plot_smooth()
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 post(self):
a = np.loadtxt('freq.dat')
ks = a[:, 1:4]
omega = a[:, 4:]
b = np.loadtxt('phon_lifetime.dat')
tao = b[:len(ks), 4:]
v = np.loadtxt(open('group_vel.dat'))[:, 4:]
n, m = v.shape
v = v.reshape([n, m / 3, 3])
v = np.linalg.norm(v, axis=2)
plot(
(omega.flatten(), 'Frequency (THz)'), (v.flatten(),
'Group Velocity (nm/ps)'),
'v_freq.png',
grid=True,
scatter=True)
tl.to_txt(['freq', 'vg'],
np.c_[omega.flatten(), v.flatten()], 'v_freq.txt')
plot(
(omega.flatten(), 'Frequency (THz)'), (tao.flatten(),
'Relaxation Time (ps)'),
'tao_freq.png',
grid=True,
scatter=True,
logy=True)
def draw_tau(self):
try:
w = get_w_final('..')
q = get_qpoints('..')
omega = get_omega('..')
tau = get_tau('..')
plot(
(omega.flatten(), 'Frequency (THz)'), (w.flatten(),
'Scatter Rate (THz)'),
'scatter_freq.png',
grid=True,
scatter=True,
logy=True)
plot(
(omega.flatten(), 'Frequency (THz)'), (tau.flatten(),
'Relaxation Time (ps)'),
'tau_freq.png',
grid=True,
scatter=True,
logy=True)
to_txt(['freq', 'tau'],
np.c_[omega.flatten(), tau.flatten()], 'tao_freq.txt')
r = []
for i, qq in enumerate(q):
c = tau[i]
d = omega[i]
for j, cc in enumerate(c):
r.append([qq[0], qq[1], qq[2], d[j], c[j]])
to_txt(['q1', 'q2', 'q3', 'f(THz)', 'tao(ps)'], r, 'q_tao.txt')
except Exception as e:
pass
def draw_gv(self):
try:
omega = get_omega('..')
tau = get_tau('..')
v = get_v('..')
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')
l = v * tau
y = (l.flatten(), 'Mean Free Path (nm)')
plot(
(omega.flatten(), 'Frequency (THz)'),
y,
'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)
def calculateDos(self):
totalVcf = np.zeros([self.totalStep, 4])
for i in range(self.natom):
print("atom", i)
vcf = self.correlate_atom(i)
totalVcf[:, :3] += vcf
totalVcf[:, 3] = np.average(totalVcf[:, :3], axis=1)
# normalization
vcf0 = totalVcf[0].copy()
for i, vcf in enumerate(totalVcf):
totalVcf[i] /= vcf0
totalStep = self.totalStep
data = np.c_[self.times, totalVcf]
tl.to_txt(['correlation_time(ps)', 'vcaf_x', 'vcaf_y',
'vcaf_z', 'vcaf_av'], data[:totalStep / 2], 'VACF.txt')
totalDos = np.abs(rfft(totalVcf, axis=0))[:totalStep / 2]
data = np.c_[self.freq, totalDos]
tl.to_txt(['Freq_THz', 'vdos_x', 'vdos_y',
'vdos_z', 'vdos_av'], data, 'VDOS.txt')
print('VACF and VDOS caculated OK')
plot_dos()
plot_vacf()
plot_smooth()
def dumpkappa(self):
import h5py
m = self.m
filename = "kappa-m%s.hdf5" % ''.join(map(str, m.kpoints))
f = h5py.File(filename)
kappa = f['kappa']
T = f['temperature']
tl.to_txt(['temperature', 'kappa'], np.c_[T, kappa[:, 0]], 'kappa.txt')
plot(
(np.array(T), 'Temperature (K)'),
(np.array(kappa),
'Thermal Conductivity (W/mK)'),
filename='kT.png')
def reduce(self):
files = ls("tmp/result.txt*")
omega = []
trans = []
dos = []
for file in files:
print(file)
# using this but loadtxt because there may be data that missing
# some columns when there are nan
result = self.read(file)
omega = np.r_[omega, result[:, 0]]
trans = np.r_[trans, result[:, 1]]
dos = np.r_[dos, result[:, 2]]
omega = np.array(omega).flatten().T
f = omega.argsort()
omega = omega[f]
trans = np.array(trans).flatten().T[f]
dos = np.array(dos).flatten().T[f]
to_txt(['omega', 'trans', 'dos'], np.c_[omega, trans, dos],
'result.txt')
def post(self):
# 1eV = 8049 cm^(-1) => 1000emV=8049 cm-1 => cm-1/meV=1000/8049
# 1cm^(-1) = 3 * 10^(10) hz =>Hz*cm=1/3e10
# a cm^-1=b THz =>a=b *1e12 Hz*cm
# a meV = b cm^-1 => a = b cm-1/meV
# omcm=omega*521.471？
self.reduce()
result = self.read('result.txt')
omega = result[:, 0]
trans = result[:, 1]
dos = result[:, 2]
omcm = omega * 1e12 * 1 / 3e10
omme = omcm * 1e12 * 6.6260755e-34 / 1.6e-19 * 1000
T = self.m.T
centerm = self.preCenter()
V = np.linalg.det(centerm.atoms.cell)
c = capacity(omega, T, V)
j = c * trans / 2.0 / np.pi
dm = omega[1] - omega[0]
kappa = j.cumsum() * dm
to_txt([
'Frequency (THz)', 'Frequency (cm^-1)', 'Frequency (meV)',
'Phonon Transmission', 'Phonon Density of State',
'Mode Capacity (J/m^3/K)', 'Mode Thermal Conductance (W/m^2/K)',
'Accumulate Thermal Conductance (W/m^2/K)'
], np.c_[omega, omcm, omme, trans, dos, c, j, kappa],
'transmission.txt')
f = self.read('transmission.txt')
#from aces.algorithm.smooth import savitzky_golay
plot([f[:, 0], 'Frequency (THz)'],
[f[:, 4], 'Phonon Density of State'], 'green_dos.png')
plot([f[:, 0], 'Frequency (THz)'], [f[:, 3], 'Phonon Transmission'],
'green_transmission.png')
#plot([f[:,0],'Frequency (THz)'],[savitzky_golay(f[:,3],11,3),'Phonon Transmission'],'smooth_transmission.png')
plot([f[:, 0], 'Frequency (THz)'],
[f[:, 6], 'Mode Thermal Conductance (W/m^2/K)'],
'green_mode_conductance.png')
def post(self):
a = np.loadtxt('freq.dat')
ks = a[:, 1:4]
omega = a[:, 4:]
b = np.loadtxt('phon_lifetime.dat')
tao = b[:len(ks), 4:]
v = np.loadtxt(open('group_vel.dat'))[:, 4:]
n, m = v.shape
v = v.reshape([n, m / 3, 3])
v = np.linalg.norm(v, axis=2)
plot((omega.flatten(), 'Frequency (THz)'),
(v.flatten(), 'Group Velocity (nm/ps)'),
'v_freq.png',
grid=True,
scatter=True)
tl.to_txt(['freq', 'vg'], np.c_[omega.flatten(),
v.flatten()], 'v_freq.txt')
plot((omega.flatten(), 'Frequency (THz)'),
(tao.flatten(), 'Relaxation Time (ps)'),
'tao_freq.png',
grid=True,
scatter=True,
logy=True)
def run(method,begin,timestep,conti,excRate,swapEnergyRate,upP,deta,tcfactor,fourierTc ,computeTc ,corRate ,kb ,T,xp,yp,zp,enforceThick,thick,zfactor,S,box,**rest):
p=profile()
p.method=method
lx,ly,lz=box[-3:]
if method=='greenkubo':
f=open('result.txt','w')
if(computeTc):
os.popen("tail -2000 kappa.txt>tailKp.txt 2>err");
df=pd.read_csv("tailKp.txt",sep=' ',header=None)
kx=np.average(np.array(df)[:,1],axis=0)
elif(fourierTc):
v=lx*ly*lz;
factor=corRate*timestep/(v*kb*T*T)*zfactor*tcfactor;
SRCHOME=dirname(__FILE__)
kx=os.popen("%s/correlation/corr factor"%SRCHOME).read();#generate a correlation file named jcor.txt
else:
gk_result=os.popen("tail -1 fileKappa 2>err").read()
kx=gk_result.split()[1]
f.write("kappa_src=%f\n"%kx);
return
flux_src=p.getFlux(begin,timestep,S,conti,lz,excRate,swapEnergyRate)[0]
aveC,aveN,aveTemp,avejx=p.getTempProfile(begin,upP,deta,S,tcfactor,zfactor)
slope,flux_bulk=p.sslope(aveC,aveTemp,aveN,avejx,upP,deta,S);
kappa_src=flux_src/slope*tcfactor*zfactor;
kappa_bulk=flux_bulk/slope*tcfactor*zfactor;
f=open('result.txt','w')
f.write('method:'+method+'\n');
f.write("kappa_src=%f\n"%(kappa_src));
f.close()
numS=0;
n=len(aveC)-3
slopes=np.zeros(n)
J_bulks=np.zeros(n)
J_bulkcs=np.zeros(n)
if(method=="muller" or method=="inject"):
for upP in range(1,n/4):
s=p.mullerSlope(aveC,aveTemp,aveN,avejx,upP);
slopes[numS],J_bulks[numS],J_bulkcs[numS]=s
numS+=1
if(method=="nvt"):
for upP in range(1,n/2):
s=p.nvtSlope(aveC,aveTemp,aveN,avejx,upP);
slopes[numS],J_bulks[numS],J_bulkcs[numS]=s
numS+=1
kappa_src=flux_src/slopes*tcfactor*zfactor;
flux_bulk=J_bulks/(deta*S);
flux_bulkc=J_bulkcs/(deta*S);
kappa_bulk=flux_bulk/slopes*tcfactor*zfactor;
kappa_bulkc=flux_bulkc/slopes*tcfactor*zfactor;
data=np.c_[np.arange(n)+1,kappa_src,kappa_bulk,kappa_bulkc,np.ones(n)*flux_src,flux_bulk,flux_bulkc,slopes]
tools.to_txt('upP kappa_src kappa_bulk kappa_bulkc flux_src flux_bulk flux_bulkc slope'.split(' '),data[:numS],"scan.txt")
def run(method, begin, timestep, conti, excRate, swapEnergyRate, upP, deta,
tcfactor, fourierTc, computeTc, corRate, kb, T, xp, yp, zp,
enforceThick, thick, zfactor, S, box, **rest):
p = profile()
p.method = method
lx, ly, lz = box[-3:]
if method == 'greenkubo':
f = open('result.txt', 'w')
if (computeTc):
os.popen("tail -2000 kappa.txt>tailKp.txt 2>err")
df = pd.read_csv("tailKp.txt", sep=' ', header=None)
kx = np.average(np.array(df)[:, 1], axis=0)
elif (fourierTc):
# v=lx*ly*lz;
# factor=corRate*timestep/(v*kb*T*T)*zfactor*tcfactor;
SRCHOME = tools.dirname(__file__)
kx = os.popen(
"%s/correlation/corr factor" %
SRCHOME).read() # generate a correlation file named jcor.txt
else:
gk_result = os.popen("tail -1 fileKappa 2>err").read()
kx = gk_result.split()[1]
f.write("kappa_src=%f\n" % kx)
return
flux_src = p.getFlux(begin, timestep, S, conti, lz, excRate,
swapEnergyRate)[0]
aveC, aveN, aveTemp, avejx = p.getTempProfile(begin, upP, deta, S,
tcfactor, zfactor)
slope, flux_bulk = p.sslope(aveC, aveTemp, aveN, avejx, upP, deta, S)
kappa_src = flux_src / slope * tcfactor * zfactor
kappa_bulk = flux_bulk / slope * tcfactor * zfactor
f = open('result.txt', 'w')
f.write('method:' + method + '\n')
f.write("kappa_src=%f\n" % (kappa_src))
f.close()
numS = 0
n = len(aveC) - 3
slopes = np.zeros(n)
J_bulks = np.zeros(n)
J_bulkcs = np.zeros(n)
if (method == "muller" or method == "inject"):
for upP in range(1, n / 4):
s = p.mullerSlope(aveC, aveTemp, aveN, avejx, upP)
slopes[numS], J_bulks[numS], J_bulkcs[numS] = s
numS += 1
if (method == "nvt"):
for upP in range(1, n / 2):
s = p.nvtSlope(aveC, aveTemp, aveN, avejx, upP)
slopes[numS], J_bulks[numS], J_bulkcs[numS] = s
numS += 1
kappa_src = flux_src / slopes * tcfactor * zfactor
flux_bulk = J_bulks / (deta * S)
flux_bulkc = J_bulkcs / (deta * S)
kappa_bulk = flux_bulk / slopes * tcfactor * zfactor
kappa_bulkc = flux_bulkc / slopes * tcfactor * zfactor
data = np.c_[np.arange(n) + 1, kappa_src, kappa_bulk, kappa_bulkc,
np.ones(n) * flux_src, flux_bulk, flux_bulkc, slopes]
tools.to_txt(
('upP kappa_src kappa_bulk' +
'kappa_bulkc flux_src flux_bulk flux_bulkc slope').split(' '),
data[:numS], "scan.txt")
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 post(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:
kappa = np.loadtxt('BTE.cumulative_kappaVsOmega_tensor')
with fig("atc_freq.png"):
pl.plot(kappa[:, 0], kappa[:, 1], label="${\kappa_{xx}}$")
pl.plot(kappa[:, 0], kappa[:, 5], label="${\kappa_{xx}}$")
pl.plot(kappa[:, 0], kappa[:, 9], label="${\kappa_{xx}}$")
pl.xlabel("Frequency (THz)")
pl.ylabel("Cumulative Thermal Conductivity(W/mK)")
with fig("tc_freq.png"):
pl.plot(
kappa[:, 0],
np.gradient(kappa[:, 1]),
label="${\kappa_{xx}}$")
pl.plot(
kappa[:, 0],
np.gradient(kappa[:, 5]),
label="${\kappa_{xx}}$")
pl.plot(
kappa[:, 0],
np.gradient(kappa[:, 9]),
label="${\kappa_{xx}}$")
pl.xlabel("Frequency (THz)")
pl.ylabel("Cumulative Thermal Conductivity(W/mK)")
except Exception as e:
print(e)
try:
g = np.loadtxt('../BTE.gruneisen')
y = (g.flatten(), 'Gruneisen')
plot(
(omega.flatten(), 'Frequency (THz)'),
y,
'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)
self.draw_gv()
self.draw_branch_scatter()
self.draw_tau()
cd('..')
def drawlifetime(self):
a = np.loadtxt('allnma.txt', skiprows=1)
om = a[:, 3]
tao = a[:, 6] # np.abs(1/a[:,5])
tao[np.abs(om) < 1e-6] = 0.0
n = len(tao)
filter = tao < 1e5
om = om[filter]
tao = tao[filter] / 6.28
plot((om, 'Frequency (THz)'), (tao, 'Relaxation Time (ps)'),
'tao_freq.png',
grid=True,
scatter=True,
logy=True)
tl.to_txt(['freq', 'tao'], np.c_[om, tao], 'tao_freq.txt')
v = np.loadtxt('vqpoints/v.txt')[:n, 4]
v = v[filter]
l = v * tao
tl.to_txt(['freq', 'lamda'], np.c_[om[om.argsort()], l[om.argsort()]],
'lamda_freq.txt')
plot((om, 'Frequency (THz)'), (l, 'Mean Free Path (Angstrom)'),
'lamda_freq.png',
grid=True,
scatter=True,
logy=True)
T = self.m.T
w = om * 1e12 * 2.0 * np.pi
from ase import io
atoms = io.read('POSCAR')
cs = self.m.correlation_supercell
V = np.linalg.det(atoms.cell * cs)
m = self.m
# print m.enforceThick,m.thick,atoms.cell[2,2]
if m.enforceThick:
V *= m.thick / atoms.cell[2, 2]
c = hbar * w * (BE(w, T + 0.005) - BE(w, T - 0.005)) * 100.0 / V * 1e30
tl.to_txt(['freq', 'capacity(J/K)'], np.c_[om[om.argsort()],
c[om.argsort()]],
'capacity_freq.txt')
plot((om[om.argsort()], 'Frequency (THz)'),
(c[om.argsort()], 'Mode Specific Heat (J/K)'),
'capacity_freq.png',
grid=True,
linewidth=2)
tl.to_txt(['freq', 'cumsumcapacity'], np.c_[om[om.argsort()],
c[om.argsort()].cumsum()],
'cumsumcapacity_freq.txt')
plot((om[om.argsort()], 'Frequency (THz)'),
(c[om.argsort()].cumsum(), 'Acummulate Specific Heat (J/K)'),
'cumsumcapacity_freq.png',
grid=True,
linewidth=2)
k = l * 1e-10 * c * v * 1e-10 / 1e-12
tl.to_txt(['freq', 'kappa'], np.c_[om[om.argsort()], k[om.argsort()]],
'kappa_freq.txt')
plot((om, 'Frequency (THz)'), (k, 'Mode Themal Conductivity (W/mK)'),
'kappa_freq.png',
grid=True,
scatter=True,
logy=True,
logx=False)
tl.to_txt(['freq', 'cumsumkappa'], np.c_[om[om.argsort()],
k[om.argsort()].cumsum()],
'cumsumkappa_freq.txt')
plot((om[om.argsort()], 'Frequency (THz)'),
(k[om.argsort()].cumsum(),
'Acummulate Themal Conductivity (W/mK)'),
'cumsumkappa_freq.png',
grid=True,
linewidth=2)
tl.to_txt(['lamda', 'cumsumkappa'], np.c_[l[l.argsort()],
k[l.argsort()].cumsum()],
'cumsumkappa_lamda.txt')
plot(
(l[l.argsort()], 'Mean Free Path (Angstrom)'),
(k[l.argsort()].cumsum(), 'Acummulate Themal Conductivity (W/mK)'),
'cumsumkappa_lamda.png',
grid=True,
linewidth=2)
def drawlifetime(self):
a = np.loadtxt('allnma.txt', skiprows=1)
om = a[:, 3]
tao = a[:, 6] # np.abs(1/a[:,5])
tao[np.abs(om) < 1e-6] = 0.0
n = len(tao)
filter = tao < 1e5
om = om[filter]
tao = tao[filter] / 6.28
plot(
(om, 'Frequency (THz)'), (tao, 'Relaxation Time (ps)'),
'tao_freq.png',
grid=True,
scatter=True,
logy=True)
tl.to_txt(['freq', 'tao'], np.c_[om, tao], 'tao_freq.txt')
v = np.loadtxt('vqpoints/v.txt')[:n, 4]
v = v[filter]
l = v * tao
tl.to_txt(['freq', 'lamda'], np.c_[om[om.argsort()], l[om.argsort()]],
'lamda_freq.txt')
plot(
(om, 'Frequency (THz)'), (l, 'Mean Free Path (Angstrom)'),
'lamda_freq.png',
grid=True,
scatter=True,
logy=True)
T = self.m.T
w = om * 1e12 * 2.0 * np.pi
from ase import io
atoms = io.read('POSCAR')
cs = self.m.correlation_supercell
V = np.linalg.det(atoms.cell * cs)
m = self.m
# print m.enforceThick,m.thick,atoms.cell[2,2]
if m.enforceThick:
V *= m.thick / atoms.cell[2, 2]
c = hbar * w * (BE(w, T + 0.005) - BE(w, T - 0.005)) * 100.0 / V * 1e30
tl.to_txt(['freq',
'capacity(J/K)'], np.c_[om[om.argsort()], c[om.argsort()]],
'capacity_freq.txt')
plot(
(om[om.argsort()], 'Frequency (THz)'),
(c[om.argsort()], 'Mode Specific Heat (J/K)'),
'capacity_freq.png',
grid=True,
linewidth=2)
tl.to_txt(['freq', 'cumsumcapacity'],
np.c_[om[om.argsort()], c[om.argsort()].cumsum()],
'cumsumcapacity_freq.txt')
plot(
(om[om.argsort()], 'Frequency (THz)'),
(c[om.argsort()].cumsum(), 'Acummulate Specific Heat (J/K)'),
'cumsumcapacity_freq.png',
grid=True,
linewidth=2)
k = l * 1e-10 * c * v * 1e-10 / 1e-12
tl.to_txt(['freq', 'kappa'], np.c_[om[om.argsort()], k[om.argsort()]],
'kappa_freq.txt')
plot(
(om, 'Frequency (THz)'), (k, 'Mode Themal Conductivity (W/mK)'),
'kappa_freq.png',
grid=True,
scatter=True,
logy=True,
logx=False)
tl.to_txt(['freq', 'cumsumkappa'],
np.c_[om[om.argsort()], k[om.argsort()].cumsum()],
'cumsumkappa_freq.txt')
plot(
(om[om.argsort()], 'Frequency (THz)'),
(k[om.argsort()].cumsum(),
'Acummulate Themal Conductivity (W/mK)'),
'cumsumkappa_freq.png',
grid=True,
linewidth=2)
tl.to_txt(['lamda', 'cumsumkappa'],
np.c_[l[l.argsort()], k[l.argsort()].cumsum()],
'cumsumkappa_lamda.txt')
plot(
(l[l.argsort()], 'Mean Free Path (Angstrom)'),
(k[l.argsort()].cumsum(), 'Acummulate Themal Conductivity (W/mK)'),
'cumsumkappa_lamda.png',
grid=True,
linewidth=2)
