def drawdos(self):
from aces.ElectronicDOS.electronicdos import ElectronicDOS
doscar = ElectronicDOS()
#orbital_dos = doscar.sum_ms_dos()
## Create a list of each atom type to sum over.
#type_list = []
#n = 0
#for i in range(len(doscar.unit_cell.atom_types)):
# type_list.append([])
# for j in range(doscar.unit_cell.atom_types[i]):
# type_list[i].append(n)
# n += 1
## Sum dos over sets of atoms.
#partial_dos = doscar.sum_site_dos(type_list,orbital_dos)
dos = doscar.write_dos([doscar.tot_dos])
write(dos, 'dos.txt')
dos = np.loadtxt('dos.txt')
f = shell_exec("grep fermi OUTCAR|tail -1")
from aces.scanf import sscanf
f = sscanf(f, "E-fermi : %f XC(G=0):")
with fig("dos.png"):
pl.xlabel("Energy-Ef (eV)")
pl.ylabel("DOS")
pl.plot(dos[:, 0] - f, dos[:, 1], lw=2)
pl.xlim([-4, 4])
def grtao(self):
cd('T300K')
# 画格林艾森系数与驰豫时间的关系
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
omega = np.loadtxt('../BTE.omega') / (2.0 * np.pi)
w[omega < omega.flatten().max() * 0.005] = float('nan')
tao = 1.0 / w + 1e-6
g = np.loadtxt('../BTE.gruneisen')
with fig("gruneisen_tao.png"):
pl.semilogy(
g.flatten(),
tao.flatten(),
ls='.',
marker='.',
color='r',
markersize=10)
pl.ylabel('Relaxation Time (ps)')
pl.xlabel('Gruneisen Coeffecient')
pl.xlim([-10, 5])
pl.ylim([0, 1e4])
def drawdos(self):
from aces.ElectronicDOS.electronicdos import ElectronicDOS
doscar = ElectronicDOS()
# orbital_dos = doscar.sum_ms_dos()
# Create a list of each atom type to sum over.
# type_list = []
# n = 0
# for i in range(len(doscar.unit_cell.atom_types)):
# type_list.append([])
# for j in range(doscar.unit_cell.atom_types[i]):
# type_list[i].append(n)
# n += 1
# Sum dos over sets of atoms.
# partial_dos = doscar.sum_site_dos(type_list,orbital_dos)
dos = doscar.write_dos([doscar.tot_dos])
tl.write(dos, 'dos.txt')
dos = np.loadtxt('dos.txt')
f = tl.shell_exec("grep fermi OUTCAR|tail -1")
from aces.scanf import sscanf
f = sscanf(f, "E-fermi : %f XC(G=0):")
with fig("dos.png"):
pl.xlabel("Energy-Ef (eV)")
pl.ylabel("DOS")
pl.plot(dos[:, 0] - f, dos[:, 1], lw=2)
pl.xlim([-4, 4])
def kappat(self):
a = np.loadtxt("BTE.KappaTensorVsT_CONV")
import matplotlib as mpl
mpl.rcParams['axes.color_cycle'] = ['#e24a33', '#2A749A', '#988ed5']
with fig('T_kappa.png', legend=True):
ts = a[:, 0]
fil = ts <= 800
ts = a[fil, 0]
k1 = 1.0 / 3 * (a[fil, 1] + a[fil, 5] + a[fil, 9])
pl.plot(ts, k1, lw=3, label="Iso-Phonon")
pl.xlabel("Tempeature (K)")
pl.ylabel('Thermal Conductivity (W/mK)')
file = ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
idx = d[:, 0] == d[np.abs(np.unique(d[:, 0])).argmin(), 0]
tao = 2.93e-14
pl.plot(d[idx, 1], d[idx, 7] * tao, lw=3, label="Iso-Electron")
pl.xlim([200, 800])
def kappat(self):
a = np.loadtxt("BTE.KappaTensorVsT_CONV")
import matplotlib as mpl
mpl.rcParams['axes.color_cycle'] = ['#e24a33', '#2A749A', '#988ed5']
with fig('T_kappa.png', legend=True):
ts = a[:, 0]
fil = ts <= 800
ts = a[fil, 0]
k1 = 1.0 / 3 * (a[fil, 1] + a[fil, 5] + a[fil, 9])
pl.plot(ts, k1, lw=3, label="Iso-Phonon")
pl.xlabel("Tempeature (K)")
pl.ylabel('Thermal Conductivity (W/mK)')
file = tl.ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
idx = d[:, 0] == d[np.abs(np.unique(d[:, 0])).argmin(), 0]
tao = 2.93e-14
pl.plot(d[idx, 1], d[idx, 7] * tao, lw=3, label="Iso-Electron")
pl.xlim([200, 800])
# -*- coding: utf-8 -*-
# @Author: YangZhou
# @Date: 2017-06-30 15:31:06
# @Last Modified by: YangZhou
# @Last Modified time: 2017-06-30 15:49:41
import pandas as pd
from aces.graph import fig, pl, setLegend
df = pd.read_csv("dos/knot/0/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("dos/knot/0/graphenedos.txt", sep=r"[ \t]", engine="python")
datas.append((dc[dc.columns[0]], dc[dc.columns[1]], 'GNR'))
with fig("dos.eps", figsize=(10, 6)):
for d in datas:
pl.plot(d[0], d[1], label=d[2])
setLegend(pl)
pl.xlabel("Frequency (THz)")
pl.ylabel("Phonon Density of States")
pl.xlim([0, 60])
# ylabel='Stress (GPa)',
# datas=datas
# ,linewidth=1
# ,filename='stress_strain.png',legend=False,grid=True)
s = []
for i, u in enumerate(c):
if not len(u) == len(c[0]):
print i
continue
s.append(u)
s = np.array(s)
x = s[0, :, 0]
y = s[:, :, 1].mean(axis=0)
dy = s[:, :, 1].std(axis=0)
y1 = y + dy / 2.0
y2 = y - dy / 2.0
with fig("ave_stress.png"):
pl.fill_between(x, y1, y2, color="#cccccc")
pl.plot(x, y, lw=2, color='r')
pl.xlabel('Strain')
pl.ylabel('Stress (GPa)')
pl.xlim([0, 0.8])
pl.ylim([0, 21])
with fig('stress_strain.png'):
for y in s[:, :, 1]:
pl.plot(x, y, color="black", alpha=0.01)
pl.xlabel('Strain')
pl.ylabel('Stress (GPa)')
pl.xlim([0, 0.8])
pl.ylim([0, 30])
def post(self):
import matplotlib as mpl
mpl.rcParams['axes.color_cycle'] = ['#e24a33', '#2A749A', '#988ed5']
file = ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
head = shell_exec("head -1 %s" % file)
if ("Ry" in head):
d[:, 0] -= self.get_outfermi()
d[:, 0] *= 13.6
T = np.unique(d[:, 1])
zz = d[np.abs(np.unique(d[:, 0])).argmin(), 0]
Tplot = [200, 300, 700]
tao = 2.93e-14
#tao=2.93e-13
#T=[300]
with fig("Seebeck.png", legend=True):
#pl.style.use('ggplot')
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("Seebeck Coefficient ($\mu$V/K)")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 4], lw=3, label="T=" + str(t) + "K")
with fig("kappa.png", legend=True): #W/mK*1/s
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
for t in Tplot:
idx = (d[:, 1] == t) * (d[:, 0] <= 1.5) * (d[:, 0] >= -1.5)
pl.plot(d[idx, 0],
d[idx, 7] * tao,
lw=3,
label="T=" + str(t) + "K")
pl.xlim([-1.5, 1.5])
with fig("kappa_t.png"):
pl.xlabel("Temperature (K)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
idx = d[:, 0] == zz
pl.plot(d[idx, 1], d[idx, 7] * tao, lw=3)
with fig("powerfactor.png", legend=True):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$S^{2}\sigma (mW/mK^2)$")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
S = d[idx, 4] * 1e-6
sigma = d[idx, 5] * tao
pl.plot(d[idx, 0],
S * S * sigma * 1e3,
lw=3,
label="T=" + str(t) + "K")
try:
a = np.loadtxt("BTE.KappaTensorVsT_CONV")
k1 = 1.0 / 3 * (a[:, 1] + a[:, 5] + a[:, 9])
with fig("ZT.png", legend=True, ncol=1):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$ZT$")
pl.xlim([-1.5, 1.5])
pl.ylim([0, 1.0])
for t in Tplot:
idx = d[:, 1] == t
fil = a[:, 0] == t
tc = k1[fil][0]
S = d[idx, 4] * 1e-6
sigma = d[idx, 5] * tao
po = S * S * sigma
ke = d[idx, 7] * tao
pl.plot(d[idx, 0],
po * t / (ke + tc),
lw=3,
label="T=" + str(t) + "K")
except Exception as e:
print e
with fig("sigma.png", legend=True): #1/(ohm m)*1/s
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$\\sigma (10^6/\\Omega m) $")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
sigma = d[idx, 5] * tao
pl.plot(d[idx, 0],
sigma * 1e-6,
lw=3,
label="T=" + str(t) + "K")
with fig("Rh-n.png", legend=True):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$\\sigma/\\tau $")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
Rh = d[idx, 6]
n = d[idx, 2]
pl.plot(d[idx, 0], 1 / Rh, lw=3, label="Rh,T=" + str(t) + "K")
pl.plot(d[idx, 0], n, lw=3, label="nT=" + str(t) + "K")
def post(self):
import matplotlib as mpl
mpl.rcParams['axes.color_cycle'] = ['#e24a33', '#2A749A', '#988ed5']
file = tl.ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
head = tl.shell_exec("head -1 %s" % file)
if("Ry" in head):
d[:, 0] -= self.get_outfermi()
d[:, 0] *= 13.6
# T=np.unique(d[:,1])
zz = d[np.abs(np.unique(d[:, 0])).argmin(), 0]
Tplot = [200, 300, 700]
tao = 2.93e-14
# tao=2.93e-13
# T=[300]
with fig("Seebeck.png", legend=True):
# pl.style.use('ggplot')
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("Seebeck Coefficient ($\mu$V/K)")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 4], lw=3, label="T=" + str(t) + "K")
with fig("kappa.png", legend=True): # W/mK*1/s
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
for t in Tplot:
idx = (d[:, 1] == t) * (d[:, 0] <= 1.5) * (d[:, 0] >= -1.5)
pl.plot(d[idx, 0], d[idx, 7] * tao,
lw=3, label="T=" + str(t) + "K")
pl.xlim([-1.5, 1.5])
with fig("kappa_t.png"):
pl.xlabel("Temperature (K)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
idx = d[:, 0] == zz
pl.plot(d[idx, 1], d[idx, 7] * tao, lw=3)
with fig("powerfactor.png", legend=True):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$S^{2}\sigma (mW/mK^2)$")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
S = d[idx, 4] * 1e-6
sigma = d[idx, 5] * tao
pl.plot(d[idx, 0], S * S * sigma * 1e3,
lw=3, label="T=" + str(t) + "K")
try:
a = np.loadtxt("BTE.KappaTensorVsT_CONV")
k1 = 1.0 / 3 * (a[:, 1] + a[:, 5] + a[:, 9])
with fig("ZT.png", legend=True, ncol=1):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$ZT$")
pl.xlim([-1.5, 1.5])
pl.ylim([0, 1.0])
for t in Tplot:
idx = d[:, 1] == t
fil = a[:, 0] == t
tc = k1[fil][0]
S = d[idx, 4] * 1e-6
sigma = d[idx, 5] * tao
po = S * S * sigma
ke = d[idx, 7] * tao
pl.plot(d[idx, 0], po * t / (ke + tc),
lw=3, label="T=" + str(t) + "K")
except Exception as e:
print(e)
with fig("sigma.png", legend=True): # 1/(ohm m)*1/s
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$\\sigma (10^6/\\Omega m) $")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
sigma = d[idx, 5] * tao
pl.plot(d[idx, 0], sigma * 1e-6, lw=3,
label="T=" + str(t) + "K")
with fig("Rh-n.png", legend=True):
pl.xlabel("$\\mu$ (eV)")
pl.ylabel("$\\sigma/\\tau $")
pl.xlim([-1.5, 1.5])
for t in Tplot:
idx = d[:, 1] == t
Rh = d[idx, 6]
n = d[idx, 2]
pl.plot(d[idx, 0], 1 / Rh, lw=3, label="Rh,T=" + str(t) + "K")
pl.plot(d[idx, 0], n, lw=3, label="nT=" + str(t) + "K")
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('..')
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)')
pl.xlim([-5, 55])
#plot([kapitza[:,0],'dT(K)'],[kapitza[:,1],'Kapitza Conductance (W/m2K)'],'kapitza.png')