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 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 correlation(self):
m = self.m
if not exists("jin.npy"):
print "loading jin.txt"
j = np.loadtxt('jin.txt', skiprows=2)[:, 1]
np.save('jin.npy', j)
j = np.load('jin.npy')
print "loaded"
n = len(j)
if not exists("jj0.npy"):
jj0 = acf(j)[:n / 2 + 1]
np.save('jj0.npy', jj0)
jj0 = np.load('jj0.npy')[:m.aveRate]
xlo, xhi, ylo, yhi, zlo, zhi, lx, ly, lz = m.box
v = lx * ly * lz
factor = m.corRate * m.timestep / (v * m.kb * m.T *
m.T) * m.zfactor * m.tcfactor
x = range(len(jj0))
plot([x, 'Correlation Time (ps)'],
[jj0, 'Heat Flut Correlation Function'], 'acf.png')
plot([x, 'Correlation Time (ps)'],
[jj0.cumsum() * factor, 'Thermal Conductivity (W/mK)'],
'kappa.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 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 drawedos(self):
fermi = self.getfermi()
s = np.loadtxt("diam.edos", skiprows=1)
freq, dos = s[:, 0], s[:, 1]
freq -= fermi
plot((freq, 'Electron Energy (eV)'), (dos, 'Density of States'),
filename='edos.png')
def post(self):
import pandas as pd
df = pd.read_csv("strain_stress.txt",
sep=r"[ \t]",
engine="python",
skiprows=2,
header=None)
import numpy as np
df = np.array(df)
strain = df[:, 1] / df[0, 1] - 1
# convert to GPa
# metalE/metalV/(siE/siV)=metalE/siE*siV/metalV=1/u.si.E()*u.si.L()**2
tl.cd('minimize')
self.m.postMini()
tl.cd('..')
pxx = -df[:, 2] * 1e5 * 1e-9 * self.m.zfactor
from aces.graph import plot
plot((strain, 'Strain'), (pxx, 'Stress (GPa)'),
'stress.png',
linewidth=2,
grid=True)
dp = pd.DataFrame()
dp['Strain'] = strain
dp['Stress_GPa'] = pxx
dp.to_csv('cal_stress.txt', sep='\t', index=False, float_format="%f")
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(
strain[:50], pxx[:50])
self.YoungsModulus = slope
tl.write(slope, 'YoungsModulus.txt')
def drawedos(self):
fermi = self.getfermi()
s = np.loadtxt("diam.edos", skiprows=1)
freq, dos = s[:, 0], s[:, 1]
freq -= fermi
plot(
(freq, 'Electron Energy (eV)'), (dos, 'Density of States'),
filename='edos.png')
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 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'] to_txt(['temperature','kappa'],np.c_[T,kappa[:,0]],'kappa.txt') from aces.graph import plot plot((np.array(T),'Temperature (K)'),(np.array(kappa),'Thermal Conductivity (W/mK)'),filename='kT.png')
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 drawV(self):
data =parseyaml('mesh.yaml')
file=open("v.txt",'w')
for phonon in data['phonon']:
qp=phonon['q-position']
for band in phonon['band']:
frequency=band['frequency']
v=np.array(band['group_velocity'])
v=np.linalg.norm(v)
print >>file,"%s\t%f\t%f"%('\t'.join(map(str,qp)),frequency,v)
file.close()
v=np.loadtxt('v.txt')
plot((v[:,3],'Frequency (THz)'),(v[:,4],'Group Velocity (Angstrom/ps)'),'v_freq.png',grid=True,scatter=True)
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 getvqpoints(self,q):
self.generate_vqconf(q)
passthru(config.phonopy+"--tolerance=1e-4 q.conf")
data =parseyaml('qpoints.yaml')
file=open("v.txt",'w')
for phonon in data['phonon']:
qp=phonon['q-position']
for band in phonon['band']:
frequency=band['frequency']
v=np.array(band['group_velocity'])
v=np.linalg.norm(v)
print >>file,"%s\t%f\t%f"%('\t'.join(map(str,qp)),frequency,v)
file.close()
v=np.loadtxt('v.txt')
plot((v[:,3],'Frequency (THz)'),(v[:,4],'Group Velocity (Angstrom/ps)'),'v_freq.png',grid=True,scatter=True)
def lifetime(self):
xx = np.loadtxt('linewidth.elself', skiprows=2)
E = xx[:, 2]
a = E.argsort()
h = 4.13566743e-15 # eV s
h = h * 1000 * 1e12 # meV ps
pi2 = 3.14159265359 * 2.0
hbar = h / pi2
linewidth = xx[:, 3] # meV
lifetime = hbar / linewidth
plot((E[a], 'Electron Energy (eV)'),
(lifetime[a], 'Electron Lifetime (ps)'),
filename='linewidth_elself.png')
hist, bin_edges = np.histogram(E, density=True, bins=200)
plot((bin_edges[:-1], 'Electron Energy (eV)'),
(hist, 'Density of States'),
filename='fine_edos.png')
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 test(self):
dm = .1
omega = np.arange(dm, 60, dm) #THz
factor = 1e12**2 * 1e-20 * 1e-3 / 1.6e-19 / 6.23e23
energies = (omega * 2.0 * np.pi)**2 * factor
#energies=np.arange(0,10,.01)
h = -np.array((-2, 1, 0, 1, -2, 1, 0, 1, -2)).reshape((3, 3))
h1 = -np.array((-2, 1, 1, -2)).reshape((2, 2))
#x=1.0/np.sqrt(2)
#h1=h=-np.array((-2,x,0,0,x,-1,x,0,0,x,-2,x,0,0,x,-1)).reshape((4,4))
#energies = np.arange(-3, 3, 0.1)
calc = TransportCalculator(h=h, h1=h1, energies=energies, dos=True)
T = calc.get_transmission()
#print T
dos = calc.get_dos() * omega
plot([omega, 'Frequency (THz)'], [T, 'Transmission'],
'test_green_transmission.png')
plot([omega, 'Frequency (THz)'], [dos, 'Phonon Density of State'],
'test_green_dos.png')
def lifetime(self):
xx = np.loadtxt('linewidth.elself', skiprows=2)
E = xx[:, 2]
a = E.argsort()
h = 4.13566743e-15 # eV s
h = h * 1000 * 1e12 # meV ps
pi2 = 3.14159265359 * 2.0
hbar = h / pi2
linewidth = xx[:, 3] # meV
lifetime = hbar / linewidth
plot(
(E[a], 'Electron Energy (eV)'), (lifetime[a],
'Electron Lifetime (ps)'),
filename='linewidth_elself.png')
hist, bin_edges = np.histogram(E, density=True, bins=200)
plot(
(bin_edges[:-1], 'Electron Energy (eV)'), (hist,
'Density of States'),
filename='fine_edos.png')
def drawV(self):
data = parseyaml('mesh.yaml')
file = open("v.txt", 'w')
for phonon in data['phonon']:
qp = phonon['q-position']
for band in phonon['band']:
frequency = band['frequency']
v = np.array(band['group_velocity'])
v = np.linalg.norm(v)
print >> file, "%s\t%f\t%f" % ('\t'.join(map(str, qp)),
frequency, v)
file.close()
v = np.loadtxt('v.txt')
plot(
(v[:, 3], 'Frequency (THz)'), (v[:, 4],
'Group Velocity (Angstrom/ps)'),
'v_freq.png',
grid=True,
scatter=True)
def getvqpoints(self, q):
self.generate_vqconf(q)
passthru(config.phonopy + "--tolerance=1e-4 q.conf")
data = parseyaml('qpoints.yaml')
file = open("v.txt", 'w')
for phonon in data['phonon']:
qp = phonon['q-position']
for band in phonon['band']:
frequency = band['frequency']
v = np.array(band['group_velocity'])
v = np.linalg.norm(v)
print >> file, "%s\t%f\t%f" % ('\t'.join(map(str, qp)),
frequency, v)
file.close()
v = np.loadtxt('v.txt')
plot(
(v[:, 3], 'Frequency (THz)'), (v[:, 4],
'Group Velocity (Angstrom/ps)'),
'v_freq.png',
grid=True,
scatter=True)
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 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):
#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？
result = np.loadtxt("result.txt")
omeage = result[:, 0]
trans = result[:, 1]
dos = result[:, 2]
omcm = omega * 1e12 * 1 / 3e10
omme = omcm * 1e12 * 6.6260755e-34 / 1.6e-19 * 1000
w = omega * 1e12 * 2.0 * np.pi
T = self.m.T
centerm = self.preCenter()
V = np.linalg.det(centerm.atoms.cell)
c = hbar * w * (BE(w, T + 0.005) - BE(w, T - 0.005)) * 100.0 / V * 1e30
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 = np.loadtxt('transmission.txt', skiprows=1)
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)'],
[f[:, 6], 'Mode Thermal Conductance (W/m^2/K)'],
'green_mode_conductance.png')
def drawpedos(self):
s = np.loadtxt("diam.edos", skiprows=1)
freq, dos = s[:, 0], s[:, 1]
plot((freq, 'Electron Energy (eV)'), (dos, 'Density of States'),
filename='edos.png')
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)
var=abs(s[j+n,1]-s[j+n-1,1]) if var>2.5 and var>s[j:j+n,1].std(): if len(v)>0 and j-v[-1]<n:continue v.append(j+n) return np.array(v) i=0 for T in range(100,300,40): for seed in range(100,110): s=np.loadtxt(str(i)+"/cal_stress.txt",skiprows=1) v=getBreakStrains(s) breakstrains.append(v) if abs(s[-1,1])<.5: breaktimes.append(len(v)) # break up else: breaktimes.append(-len(v)) #not break up i+=1 print s[v,0],breaktimes[-1] for u in s: pass from aces.graph import series,plot series(xlabel='Strain', ylabel='Stress (GPa)', datas=datas ,linewidth=1 ,filename='stress_strain.png',legend=False,grid=True) x=c[:,0,:].mean(axis=0) y=c[:,1,:].mean(axis=0) plot([x,'Strain'],[y,'Stress (GPa)'],filename='ave_stress.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
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
def drawDos(self):
freq, dos = self.readdos()
freq /= 33.367
plot(
(freq, 'Frequency (THz)'), (dos, 'Density of States'),
filename='total_dos.png')
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)
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
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
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)
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
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)
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)
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 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
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 drawDos(self):
freq, dos = self.readdos()
freq /= 33.367
plot((freq, 'Frequency (THz)'), (dos, 'Density of States'),
filename='total_dos.png')
def fftac(self):
x = np.loadtxt('aveac.txt')
N = len(x) / 100
y = np.fft.rfft(x[:, 1])[:N / 2 + 1]
plot([np.arange(N / 2 + 1), 'Frequency (Thz)'],
[(y * y.conjugate()).real, 'Power Spectrum '], 'powerspect.png')
def drawpedos(self):
s = np.loadtxt("diam.edos", skiprows=1)
freq, dos = s[:, 0], s[:, 1]
plot(
(freq, 'Electron Energy (eV)'), (dos, 'Density of States'),
filename='edos.png')
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('..')
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 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)
