def kkappa(self):
dirs=ls('shengold*')
from aces.scanf import sscanf
from aces.graph import fig,pl
import matplotlib as mpl
mpl.rcParams['axes.color_cycle']=['#e24a33','#2A749A','#988ed5']
us=[]
for d in dirs:
f=shell_exec('grep ngrid %s/CONTROL'%d)
ks=sscanf(f," ngrid(:)=%d %d %d")
f=np.loadtxt('%s/BTE.kappa_tensor'%d)
print ks
if len(f.shape)==2:
x=f[-1]
else:
x=f
x=x[1:].reshape([3,3])
print x
us.append([ks,x])
with fig('kkappa_64nn.png',legend=True):
p1=filter(lambda u: u[0][0]==64,us)
k1=[]
k2=[]
k3=[]
ks=[]
for u in p1:
ks.append(u[0][1])
k1.append(u[1][0,0])
k2.append(u[1][1,1])
k3.append(u[1][2,2])
f=np.argsort(ks)
ks=np.array(ks)[f];k1=np.array(k1)[f];k2=np.array(k2)[f];k3=np.array(k3)[f];
pl.plot(ks,k1,markersize=30,linestyle='--',markeredgecolor='w',marker=".",label="${\kappa_{xx}}$")
pl.plot(ks,k2,markersize=15,linestyle='--',markeredgecolor='w',marker="v",label="${\kappa_{yy}}$")
pl.plot(ks,k3,markersize=15,linestyle='--',markeredgecolor='w',marker="^",label="${\kappa_{zz}}$")
pl.ylim([0,0.35])
pl.xlim([0,np.array(ks).max()+1])
pl.xlabel("$Nq_y$ and $Nq_z$")
pl.ylabel("Themal Conductivity (W/mK)")
with fig('kkappa_n44.png',legend=True):
p1=filter(lambda u: u[0][1]==4,us)
k1=[]
k2=[]
k3=[]
ks=[]
for u in p1:
ks.append(u[0][0])
k1.append(u[1][0,0])
k2.append(u[1][1,1])
k3.append(u[1][2,2])
f=np.argsort(ks)
ks=np.array(ks)[f];k1=np.array(k1)[f];k2=np.array(k2)[f];k3=np.array(k3)[f];
pl.plot(ks,k1,markersize=30,linestyle='--',markeredgecolor='w',marker=".",label="${\kappa_{xx}}$")
pl.plot(ks,k2,markersize=15,linestyle='--',markeredgecolor='w',marker="v",label="${\kappa_{yy}}$")
pl.plot(ks,k3,markersize=15,linestyle='--',markeredgecolor='w',marker="^",label="${\kappa_{zz}}$")
pl.ylim([0,0.35])
pl.xlim([0,np.array(ks).max()+100])
pl.xlabel("$Nq_x$")
pl.ylabel("Themal Conductivity (W/mK)")
def vtao(self):
#group velocity vs. tao using old version of shengbte
w=np.loadtxt('BTE.w_final')
w=np.abs(w)
omega=np.loadtxt('BTE.omega')/(2.0*np.pi)
w[omega<omega.flatten().max()*0.005]=float('nan')
tao=1.0/w+1e-6
v=np.loadtxt(open('BTE.v'))
n,m=v.shape
v=v.reshape([n,3,m/3])
v=np.linalg.norm(v,axis=1)
l=v*tao
l[l<1e-6]=None
with fig('tao_v.png'):
pl.semilogy(v.flatten(),tao.flatten(),linestyle='.',marker='.',color='r',markersize =5)
pl.xlabel('Group Velocity (nm/ps)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
with fig('tao_l.png'):
pl.loglog(l.flatten(),tao.flatten(),linestyle='.',marker='.',color='r',markersize =5)
pl.xlabel('Mean Free Path (nm)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
with fig('v_l.png'):
pl.semilogy(v.flatten(),l.flatten(),linestyle='.',marker='.',color='r',markersize =5)
pl.xlabel('Group Velocity (nm/ps)')
pl.ylabel('Mean Free Path (nm)')
pl.grid(True)
def kmfp(self):
def ff(p,x):
#return p[0]*(1.0-np.exp(-x**p[2]/p[1]))
return 1.0/(p[1]/x+1/p[0])-p[2]
#return p[0]*p[1]**x
def fit(x,z,p0,tt):
def errorfunc(p,x,z):
return tt(p,x)-z
from scipy.optimize import leastsq
solp, ier = leastsq(errorfunc,
p0,
args=(x,z),
Dfun=None,
full_output=False,
ftol=1e-9,
xtol=1e-9,
maxfev=100000,
epsfcn=1e-10,
factor=0.1)
return solp
dirs=ls('shengold*')
from aces.scanf import sscanf
from aces.graph import fig,pl
us=[]
for d in dirs:
f=shell_exec('grep ngrid %s/CONTROL'%d)
ks=sscanf(f," ngrid(:)=%d %d %d")
if(ks[1]!=4):continue
f=np.loadtxt('%s/BTE.cumulative_kappa_scalar'%d)
us.append([ks,f])
with fig('reduce_mfp.png',legend=True,ncol=1):
for i,u in enumerate(us):
if i <3:continue
ks,f=u
x,y=f[:,0],f[:,1]
pl.semilogx(x,y,label="Nx= %d "%ks[0],linewidth=2)
ks,f=us[-1]
x,y=f[:,0],f[:,1]
#fil=(x>0)
#p=fit(x[fil],y[fil],[1,1,1],ff)
#y1=ff(p,x)
#pl.semilogx(x,y1,label="fit of Nx= %d "%ks[0],linewidth=2)
pl.xlabel('Cutoff Mean Free Path for Phonons (Angstrom)')
pl.ylabel('Thermal Conductivity (W/mK)')
pl.grid(True)
with fig('kappa_inv_mpf_inv.png',legend=True,ncol=1):
ks,f=us[-1]
fil=x>.5
x,y=f[fil,0],f[fil,1]
xx=1/x;yy=1/y
pl.plot(xx,yy,linewidth=3,c='red',label="Nx=1024")
def ll(p,x):
return p[0]*x+p[1]
fil=xx>xx.max()/4
p=fit(xx[fil],yy[fil],[1,1,1],ll)
pl.plot(xx,ll(p,xx),lw=3,ls='dashed',label="Fitted")
pl.xlabel('1/L (1/Angstrom)')
pl.ylabel('$1/\\kappa_L$ (mK/W)')
pl.grid(True)
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 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 taoth(self):
#tao vs. direction in xy plane using old version of shengbte
w=np.loadtxt('BTE.w_final')
w=np.abs(w)
omega=np.loadtxt('BTE.omega')/(2.0*np.pi)
w[omega<omega.flatten().max()*0.005]=float('nan')
tao=1.0/w+1e-6
tao[tao>10000]=0
tao=np.nan_to_num(tao)
u=self.getQFmap()
tao=tao[u]
#为了限制q点在BZ,必须自己重新来
#qpoints_full=np.loadtxt('BTE.qpoints_full')
#q=qpoints_full[:,-3:]
q=self.getQ()
with fig('tao_th.png'):
ax = pl.subplot(111, projection='polar')
N=100
th=np.linspace(0,1,N)*np.pi*2.0-np.pi
r=np.zeros_like(th)
r1=np.zeros_like(th)
theta=np.arctan2(q[:,1],q[:,0])
for i in np.arange(1):
for j,tt in enumerate(th):
if j==len(th)-1:
fil=(theta>=tt)
else:
fil=(theta>=tt) *(theta<th[j+1])
r[j]=np.nan_to_num(tao[fil].mean())
r1[j]=np.nan_to_num(fil.sum())
c = pl.plot(th, r,lw=2)
#pl.plot(th, r1,lw=2)
#c.set_alpha(0.75)
#pl.semilogy(q[:,0].flatten(),tao[:,i].flatten(),linestyle='.',marker='.',color='r',markersize =5)
pl.grid(True)
def vtao(self):
# group velocity vs. tao using old version of shengbte
w = np.loadtxt('BTE.w_final')
w = np.abs(w)
omega = np.loadtxt('BTE.omega') / (2.0 * np.pi)
w[omega < omega.flatten().max() * 0.005] = float('nan')
tao = 1.0 / w + 1e-6
v = np.loadtxt(open('BTE.v'))
n, m = v.shape
v = v.reshape([n, 3, m / 3])
v = np.linalg.norm(v, axis=1)
l = v * tao
l[l < 1e-6] = None
with fig('tao_v.png'):
pl.semilogy(
v.flatten(),
tao.flatten(),
linestyle='.',
marker='.',
color='r',
markersize=5)
pl.xlabel('Group Velocity (nm/ps)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
with fig('tao_l.png'):
pl.loglog(
l.flatten(),
tao.flatten(),
linestyle='.',
marker='.',
color='r',
markersize=5)
pl.xlabel('Mean Free Path (nm)')
pl.ylabel('Relaxation Time (ps)')
pl.grid(True)
with fig('v_l.png'):
pl.semilogy(
v.flatten(),
l.flatten(),
linestyle='.',
marker='.',
color='r',
markersize=5)
pl.xlabel('Group Velocity (nm/ps)')
pl.ylabel('Mean Free Path (nm)')
pl.grid(True)
def postbol(self):
seebeck = np.loadtxt("diam_seebeck.dat", skiprows=2)
ef = self.getfermi()
mu = seebeck[:, 0] - ef
filter = (mu < 2) * (mu > -2)
mu = mu[filter]
seebeck = seebeck[filter]
s1 = seebeck[:, 2]
s2 = seebeck[:, 6]
s3 = seebeck[:, 10]
from aces.graph import fig, pl
with fig("Seebeck.png", legend=True):
pl.plot(mu, s1, label="Sxx")
pl.plot(mu, s2, label="Syy")
pl.plot(mu, s3, label="Szz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Seebeck Coefficient (V/K)")
sigma = np.loadtxt("diam_elcond.dat")
sigma = sigma[filter]
with fig("Sigma.png", legend=True):
pl.plot(mu, sigma[:, 2], label="$\sigma$ xx")
pl.plot(mu, sigma[:, 4], label="$\sigma$ yy")
pl.plot(mu, sigma[:, 7], label="$\sigma$ zz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Electrical conductivity (1/$\Omega$/m)")
kappa = np.loadtxt("diam_kappa.dat")
kappa = kappa[filter]
with fig("kappa.png", legend=True):
pl.plot(mu, kappa[:, 2], label="$\kappa$ xx")
pl.plot(mu, kappa[:, 4], label="$\kappa$ yy")
pl.plot(mu, kappa[:, 7], label="$\kappa$ zz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Thermal Conductivity (W/m/K)")
with fig("powerfactor.png", legend=True):
pl.plot(mu, sigma[:, 2] * s1 * s1, label="Pxx")
pl.plot(mu, sigma[:, 4] * s2 * s2, label="Pyy")
pl.plot(mu, sigma[:, 7] * s3 * s3, label="Pzz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Power Factor ")
dos = np.loadtxt("diam_boltzdos.dat")
with fig("boltzdos.png"):
pl.plot(dos[:, 0], dos[:, 1])
pl.xlabel("E-Ef (eV)")
pl.ylabel("Electron Density of States (arbitrary unit)")
def postbol(self):
seebeck = np.loadtxt("diam_seebeck.dat", skiprows=2)
ef = self.getfermi()
mu = seebeck[:, 0] - ef
filter = (mu < 2) * (mu > -2)
mu = mu[filter]
seebeck = seebeck[filter]
s1 = seebeck[:, 2]
s2 = seebeck[:, 6]
s3 = seebeck[:, 10]
from aces.graph import fig, pl
with fig("Seebeck.png", legend=True):
pl.plot(mu, s1, label="Sxx")
pl.plot(mu, s2, label="Syy")
pl.plot(mu, s3, label="Szz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Seebeck Coefficient (V/K)")
sigma = np.loadtxt("diam_elcond.dat")
sigma = sigma[filter]
with fig("Sigma.png", legend=True):
pl.plot(mu, sigma[:, 2], label="$\sigma$ xx")
pl.plot(mu, sigma[:, 4], label="$\sigma$ yy")
pl.plot(mu, sigma[:, 7], label="$\sigma$ zz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Electrical conductivity (1/$\Omega$/m)")
kappa = np.loadtxt("diam_kappa.dat")
kappa = kappa[filter]
with fig("kappa.png", legend=True):
pl.plot(mu, kappa[:, 2], label="$\kappa$ xx")
pl.plot(mu, kappa[:, 4], label="$\kappa$ yy")
pl.plot(mu, kappa[:, 7], label="$\kappa$ zz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Thermal Conductivity (W/m/K)")
with fig("powerfactor.png", legend=True):
pl.plot(mu, sigma[:, 2] * s1 * s1, label="Pxx")
pl.plot(mu, sigma[:, 4] * s2 * s2, label="Pyy")
pl.plot(mu, sigma[:, 7] * s3 * s3, label="Pzz")
pl.xlabel("$\mu$-Ef (eV)")
pl.ylabel("Power Factor ")
dos = np.loadtxt("diam_boltzdos.dat")
with fig("boltzdos.png"):
pl.plot(dos[:, 0], dos[:, 1])
pl.xlabel("E-Ef (eV)")
pl.ylabel("Electron Density of States (arbitrary unit)")
def getmsd():
allpos = np.load('allpos.npy')
r = allpos - allpos[0]
r_atom = np.linalg.norm(r, axis=2)
r = r_atom.mean(axis=1)
f = open("msd.txt", 'w')
for i, x in enumerate(r):
print >>f, "%.3f\t%.3f" % (i * 0.25, x)
with fig('msd.png'):
pl.plot(np.arange(len(r)) * .25, r)
def getmsd():
allpos = np.load('allpos.npy')
r = allpos - allpos[0]
r_atom = np.linalg.norm(r, axis=2)
r = r_atom.mean(axis=1)
f = open("msd.txt", 'w')
for i, x in enumerate(r):
print >> f, "%.3f\t%.3f" % (i * 0.25, x)
with fig('msd.png'):
pl.plot(np.arange(len(r)) * .25, r)
def post(self):
file = ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
d[:, 0] -= self.get_outfermi()
d[:, 0] *= 13.6
T = np.unique(d[:, 1])
T = [300]
with fig("Seebeck.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("Seebeck Coefficient ($\mu$V/K)")
for t in [T[-1]]:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 4], label="T=" + str(t))
with fig("kappa.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
for t in [T[-1]]:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 7], label="T=" + str(t))
with fig("powerfactor.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$S^{2}\sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
S = d[idx, 4]
sigma = d[idx, 5]
pl.plot(d[idx, 0], S * S * sigma, label="T=" + str(t))
with fig("sigma.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
sigma = d[idx, 5]
pl.plot(d[idx, 0], sigma, label="T=" + str(t))
with fig("Rh-n.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
Rh = d[idx, 6]
n = d[idx, 2]
pl.plot(d[idx, 0], 1 / Rh, label="T=" + str(t))
pl.plot(d[idx, 0], n, label="T=" + str(t))
def post(self):
file = tl.ls("*.trace")[0]
d = np.loadtxt(file, skiprows=1)
d[:, 0] -= self.get_outfermi()
d[:, 0] *= 13.6
T = np.unique(d[:, 1])
T = [300]
with fig("Seebeck.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("Seebeck Coefficient ($\mu$V/K)")
for t in [T[-1]]:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 4], label="T=" + str(t))
with fig("kappa.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("Electronic Thermal Conductivity (W/mK)")
for t in [T[-1]]:
idx = d[:, 1] == t
pl.plot(d[idx, 0], d[idx, 7], label="T=" + str(t))
with fig("powerfactor.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$S^{2}\sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
S = d[idx, 4]
sigma = d[idx, 5]
pl.plot(d[idx, 0], S * S * sigma, label="T=" + str(t))
with fig("sigma.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
sigma = d[idx, 5]
pl.plot(d[idx, 0], sigma, label="T=" + str(t))
with fig("Rh-n.png"):
pl.xlabel("Energy (eV)")
pl.ylabel("$sigma/\\tau $")
for t in [T[-1]]:
idx = d[:, 1] == t
Rh = d[idx, 6]
n = d[idx, 2]
pl.plot(d[idx, 0], 1 / Rh, label="T=" + str(t))
pl.plot(d[idx, 0], n, label="T=" + str(t))
def postT(self):
a = np.loadtxt("BTE.KappaTensorVsT_CONV")
with fig('T_kappa.png', legend=True):
ts = a[:, 0]
fil = ts <= 800
k1 = a[fil, 1]
k2 = a[fil, 5]
k3 = a[fil, 9]
ts = a[fil, 0]
pl.plot(ts, k1, lw=2, label="${\kappa_{xx}}$")
pl.plot(ts, k2, lw=2, label="${\kappa_{yy}}$")
pl.plot(ts, k3, lw=2, label="${\kappa_{zz}}$")
pl.xlabel("Tempeature (K)")
pl.ylabel('Thermal Conductivity (W/mK)')
def drawpr(self):
pr()
# plot
xs = []
ys = []
for line in open('pr.txt'):
x, y = map(float, line.split())
xs.append(x)
ys.append(y)
write("%s" % (sum(ys) / len(ys)), "ave_pr.txt")
with fig('Paticipation_ratio.png'):
pl.plot(xs, ys, '.', color='red')
pl.ylim([0.0, 1.0])
pl.xlabel('Frequency (THz)')
pl.ylabel('Paticipation Ratio')
def drawpr(self):
pr()
# plot
xs = []
ys = []
for line in open('pr.txt'):
x, y = map(float, line.split())
xs.append(x)
ys.append(y)
write("%s" % (sum(ys) / len(ys)), "ave_pr.txt")
with fig('Paticipation_ratio.png'):
pl.plot(xs, ys, '.', color='red')
pl.ylim([0.0, 1.0])
pl.xlabel('Frequency (THz)')
pl.ylabel('Paticipation Ratio')
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])
def reducemsd():
msd = np.loadtxt('600K/msd.txt')
time = msd[:, 0]
aa = []
aa.append(np.loadtxt('msd.txt')[:500, 1])
aa.append(np.loadtxt('600K/msd.txt')[:, 1])
aa.append(np.loadtxt('700K/msd.txt')[:, 1])
aa.append(np.loadtxt('800K/msd.txt')[:, 1])
aa.append(np.loadtxt('900K/msd.txt')[:, 1])
aa = np.array(aa)
ll = [300, 600, 700, 800, 900]
import matplotlib
matplotlib.rcParams['legend.fontsize'] = 12
with fig('msd_T.png', legend=True):
for i, x in enumerate(aa):
pl.plot(time, x, lw=2, label="%sK" % ll[i])
pl.xlabel("Time (fs)")
pl.ylabel("Mean Square Displacement (Angstrom)")
def oldT(self):
from aces.graph import fig,pl
import matplotlib as mpl
mpl.rcParams['axes.color_cycle']=['#e24a33','#2A749A','#988ed5']
ts=np.arange(200,801,100)
a=[]
for dir in ts:
print dir
x=np.loadtxt("12844."+str(dir)+"K/BTE.kappa_tensor")
a.append(x)
a=np.array(a)
with fig('T_kappa.png',legend=True):
k1=a[:,1]
k2=a[:,5]
k3=a[:,9]
pl.plot(ts,k1,lw=3,markersize=30,linestyle='--',markeredgecolor='w',marker=".",label="${\kappa_{xx}}$")
pl.plot(ts,k2,lw=3,markersize=15,linestyle='--',markeredgecolor='w',marker="v",label="${\kappa_{yy}}$")
pl.plot(ts,k3,lw=3,markersize=15,linestyle='--',markeredgecolor='w',marker="^",label="${\kappa_{zz}}$")
pl.xlabel("Tempeature (K)")
pl.ylabel('Thermal Conductivity (W/mK)')
pl.xlim([200,800])
def taoth(self):
# tao vs. direction in xy plane using old version of shengbte
w = np.loadtxt('BTE.w_final')
w = np.abs(w)
omega = np.loadtxt('BTE.omega') / (2.0 * np.pi)
w[omega < omega.flatten().max() * 0.005] = float('nan')
tao = 1.0 / w + 1e-6
tao[tao > 10000] = 0
tao = np.nan_to_num(tao)
u = self.getQFmap()
tao = tao[u]
# 为了限制q点在BZ,必须自己重新来
# qpoints_full=np.loadtxt('BTE.qpoints_full')
# q=qpoints_full[:,-3:]
q = self.getQ()
with fig('tao_th.png'):
# ax = pl.subplot(111, projection='polar')
N = 100
th = np.linspace(0, 1, N) * np.pi * 2.0 - np.pi
r = np.zeros_like(th)
r1 = np.zeros_like(th)
theta = np.arctan2(q[:, 1], q[:, 0])
for i in np.arange(1):
for j, tt in enumerate(th):
if j == len(th) - 1:
fil = (theta >= tt)
else:
fil = (theta >= tt) * (theta < th[j + 1])
r[j] = np.nan_to_num(tao[fil].mean())
r1[j] = np.nan_to_num(fil.sum())
# c = pl.plot(th, r, lw=2)
# pl.plot(th, r1,lw=2)
# c.set_alpha(0.75)
# pl.semilogy(q[:,0].flatten(),tao[:,i].flatten()
# ,linestyle='.',marker='.',color='r',markersize =5)
pl.grid(True)
# -*- 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])
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)')
pl.xlim([-5, 55])
#plot([kapitza[:,0],'dT(K)'],[kapitza[:,1],'Kapitza Conductance (W/m2K)'],'kapitza.png')
def plotchg():
ch = getcharge()
with fig("chg.png"):
pl.imshow(ch[0])
# 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])
k, = sscanf(p, 'kappa_src=%f')
kappa.append([lx, strain, k])
i = -1
for lx, procs, nodes in [(180, 12, 1)]:
for j in range(N):
continue
i += 1
dir = "sl/lx2/" + str(i)
strain = 0.05 * j / N
p = tl.shell_exec('tail -1 %s/result.txt' % dir)
k, = sscanf(p, 'kappa_src=%f')
kappa.append([lx, strain, k])
kappa = np.array(kappa)
ms = ['D', 'o', 'x', 's', '^', 'p', '.', '+']
with fig('tc_lx.eps', legend=False, figsize=(10, 6)):
axis = pl.subplot(1, 2, 1)
strains = np.unique(kappa[:, 1])
i = -1
for s in strains:
i += 1
filter = kappa[:, 1] == s
x = kappa[filter, 0]
y = kappa[filter, 2]
axis.plot(x,
y,
marker=ms[i],
ms=8,
mec=None,
mfc='w',
n = len(d)
m = len(d[0][0])
ss = np.zeros([n, m])
i = -1
for s, v in d:
i += 1
m = len(s)
ss[i, :m] = s[:, 1]
ss = np.abs(ss) < .5
kk = ss.sum(axis=0)
s = d[0][0]
x = s[:, 0]
y = kk.astype(np.float) / len(d)
from aces.graph import setLegend
with fig('drop_position.png', legend=False, ncol=1, figsize=(14, 10)):
oldpl = pl
pl = oldpl.subplot(2, 2, 1)
c = ['g', 'r', 'b', 'orange']
dx = 0.05
for i, serie in enumerate(datas):
pl.fill_between(serie[0],
0,
serie[1],
label=serie[2],
linewidth=0,
facecolor=c[i],
alpha=.5)
pl.plot(serie[0], serie[1], label=serie[2], color=c[i], linewidth=2)
#pl.bar(serie[0]-dx*.5,serie[1],label=serie[2],color=c[i],alpha = .5,width=dx)
pl.set_xlabel('Strain')
# -*- coding: utf-8 -*-
# @Author: YangZhou
# @Date: 1970-01-01 08:00:00
# @Last Modified by: YangZhou
# @Last Modified time: 2017-06-30 15:44:36
from aces.graph import fig, pl
import numpy as np
with fig('cycle.eps'):
fi, axes = pl.subplots(1, 2, sharex=True, figsize=(10, 5))
y = np.loadtxt("strain/circle/right/9/cal_stress.txt", skiprows=1)
axes[0].plot(y[:, 0], y[:, 1], color="k", lw=2)
axes[0].set_xlabel("Strain")
axes[0].set_ylabel("Stress(GPa)")
y = np.loadtxt("strain/circle/graphene/0/cal_stress.txt", skiprows=1)
axes[1].plot(y[:, 0], y[:, 1], color="k", lw=2)
axes[1].set_xlabel("Strain")
axes[1].set_ylabel("Stress(GPa)")
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")
# -*- coding: utf-8 -*-
# @Author: YangZhou
# @Date: 2017-06-27 23:39:13
# @Last Modified by: YangZhou
# @Last Modified time: 2017-06-27 23:43:11
import numpy as np
from aces.graph import fig, pl
with fig('transmission.eps'):
fi, axes = pl.subplots(figsize=(10, 7))
ax = axes
file = "negf/knot/0/transmission.txt"
f = np.loadtxt(file, skiprows=1)
freq = f[:, 0]
trans = f[:, 3]
ax.plot(freq, trans, color="r")
fi.text(0.5, 0.04, 'Phonon Frequency (THz)', ha='center')
fi.text(0.07, 0.5, 'Phonon Transmission', va='center', rotation='vertical')
fi.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
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)
n = len(d)
m = len(d[0][0])
ss = np.zeros([n, m])
i = -1
for s, v in d:
i += 1
m = len(s)
ss[i, :m] = s[:, 1]
ss = np.abs(ss) < .5
kk = ss.sum(axis=0)
s = d[0][0]
x = s[:, 0]
y = kk.astype(np.float) / len(d)
from aces.graph import setLegend
with fig('drop.eps', legend=False, ncol=1, figsize=(10, 6)):
oldpl = pl
pl = oldpl.subplot(2, 2, 1)
c = ['g', 'r', 'b', 'orange']
dx = 0.05
for i, serie in enumerate(datas):
pl.fill_between(
serie[0],
0,
serie[1],
label=serie[2],
linewidth=0,
facecolor=c[i],
alpha=.5)
pl.plot(serie[0], serie[1],
label=serie[2], color=c[i], linewidth=2)
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
dir = "sl/nx2/" + str(i)
p = np.loadtxt(dir + '/tempAve.txt', skiprows=1)
x = np.linspace(0, 1, len(p))
data.append([x, p[:, 3], 'nx=' + str(lx)])
dt = p[10:, 3] - p[:-10, 3]
p = tl.shell_exec('tail -1 %s/result.txt' % dir)
k, = sscanf(p, 'kappa_src=%f')
dt = np.sort(np.abs(dt))[-lx:].mean()
kappa.append([lx, k, dt])
data = np.array(data)[::3]
kappa = np.array(kappa)
with fig('tc_nx.eps', figsize=(10, 6)):
# tc-nx
axis = pl.subplot(2, 2, 1)
x = kappa[:, 0]
y = kappa[:, 1]
axis.plot(
x,
y,
marker='D',
ms=12,
mec='b',
mfc='w',
mfcalt="w",
mew=1.5,
linewidth=1,
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 kmfp(self):
def ff(p, x):
# return p[0]*(1.0-np.exp(-x**p[2]/p[1]))
return 1.0 / (p[1] / x + 1 / p[0]) - p[2]
# return p[0]*p[1]**x
def fit(x, z, p0, tt):
def errorfunc(p, x, z):
return tt(p, x) - z
from scipy.optimize import leastsq
solp, ier = leastsq(
errorfunc,
p0,
args=(x, z),
Dfun=None,
full_output=False,
ftol=1e-9,
xtol=1e-9,
maxfev=100000,
epsfcn=1e-10,
factor=0.1)
return solp
dirs = ls('shengold*')
from aces.scanf import sscanf
from aces.graph import fig, pl
us = []
for d in dirs:
f = shell_exec('grep ngrid %s/CONTROL' % d)
ks = sscanf(f, " ngrid(:)=%d %d %d")
if (ks[1] != 4):
continue
f = np.loadtxt('%s/BTE.cumulative_kappa_scalar' % d)
us.append([ks, f])
with fig('reduce_mfp.png', legend=True, ncol=1):
for i, u in enumerate(us):
if i < 3:
continue
ks, f = u
x, y = f[:, 0], f[:, 1]
pl.semilogx(x, y, label="Nx= %d " % ks[0], linewidth=2)
ks, f = us[-1]
x, y = f[:, 0], f[:, 1]
# fil=(x>0)
# p=fit(x[fil],y[fil],[1,1,1],ff)
# y1=ff(p,x)
# pl.semilogx(x,y1,label="fit of Nx= %d "%ks[0],linewidth=2)
pl.xlabel('Cutoff Mean Free Path for Phonons (Angstrom)')
pl.ylabel('Thermal Conductivity (W/mK)')
pl.grid(True)
with fig('kappa_inv_mpf_inv.png', legend=True, ncol=1):
ks, f = us[-1]
fil = x > .5
x, y = f[fil, 0], f[fil, 1]
xx = 1 / x
yy = 1 / y
pl.plot(xx, yy, linewidth=3, c='red', label="Nx=1024")
def ll(p, x):
return p[0] * x + p[1]
fil = xx > xx.max() / 4
p = fit(xx[fil], yy[fil], [1, 1, 1], ll)
pl.plot(xx, ll(p, xx), lw=3, ls='dashed', label="Fitted")
pl.xlabel('1/L (1/Angstrom)')
pl.ylabel('$1/\\kappa_L$ (mK/W)')
pl.grid(True)
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 plotchg():
ch = getcharge()
with fig("chg.png"):
pl.imshow(ch[0])
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
