def full_static_screened_interaction(self):
"""Calcuate W_GG(q)"""
W_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)
t0 = time()
for iq in range(self.nibzq):
q = self.ibzq_qc[iq]
optical_limit = False
if np.abs(q).sum() < 1e-8:
q = self.q_c.copy()
optical_limit = True
df = DF(calc=self.calc,
q=q,
w=(0.,),
optical_limit=optical_limit,
nbands=self.nbands,
hilbert_trans=False,
eta=0.0001,
ecut=self.ecut*Hartree,
xc='RPA',
txt='df.out')
df.initialize()
df.calculate()
if optical_limit:
K_GG = self.V_qGG[iq].copy()
q_v = np.dot(q, self.bcell_cv)
K0 = calculate_Kc(q,
self.Gvec_Gc,
self.acell_cv,
self.bcell_cv,
self.pbc,
vcut=self.vcut)[0,0]
for iG in range(1,self.npw):
K_GG[0, iG] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
K_GG[iG, 0] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
K_GG[0,0] = K0
df_GG = np.eye(self.npw, self.npw) - K_GG*df.chi0_wGG[0]
else:
df_GG = np.eye(self.npw, self.npw) - self.V_qGG[iq]*df.chi0_wGG[0]
dfinv_GG = np.linalg.inv(df_GG)
if optical_limit:
eps = 1/dfinv_GG[0,0]
self.printtxt(' RPA macroscopic dielectric constant is: %3.3f' % eps.real)
W_qGG[iq] = dfinv_GG * self.V_qGG[iq]
self.timing(iq, t0, self.nibzq, 'iq')
if rank == 0:
if self.kernel_file is not None:
data = {'W_qGG': W_qGG}
name = self.kernel_file+'.pckl'
pickle.dump(data, open(name, 'w'), -1)
return W_qGG
def full_static_screened_interaction(self):
"""Calcuate W_GG(q)"""
W_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)
t0 = time()
for iq in range(self.nibzq):
q = self.ibzq_qc[iq]
optical_limit = False
if np.abs(q).sum() < 1e-8:
q = self.q_c.copy()
optical_limit = True
df = DF(calc=self.calc,
q=q,
w=(0., ),
optical_limit=optical_limit,
nbands=self.nbands,
hilbert_trans=False,
eta=0.0001,
ecut=self.ecut * Hartree,
xc='RPA',
txt='df.out')
df.initialize()
df.calculate()
if optical_limit:
K_GG = self.V_qGG[iq].copy()
K0 = calculate_Kc(q,
self.Gvec_Gc,
self.acell_cv,
self.bcell_cv,
self.pbc,
vcut=self.vcut)[0, 0]
for iG in range(1, self.npw):
K_GG[0, iG] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
K_GG[iG, 0] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
K_GG[0, 0] = K0
df_GG = np.eye(self.npw, self.npw) - K_GG * df.chi0_wGG[0]
else:
df_GG = np.eye(self.npw,
self.npw) - self.V_qGG[iq] * df.chi0_wGG[0]
dfinv_GG = np.linalg.inv(df_GG)
if optical_limit:
eps = 1 / dfinv_GG[0, 0]
self.printtxt(
' RPA macroscopic dielectric constant is: %3.3f' %
eps.real)
W_qGG[iq] = dfinv_GG * self.V_qGG[iq]
self.timing(iq, t0, self.nibzq, 'iq')
if rank == 0:
if self.kernel_file is not None:
data = {'W_qGG': W_qGG}
name = self.kernel_file + '.pckl'
pickle.dump(data, open(name, 'w'), -1)
return W_qGG
def get_C6_coefficient(self,
ecut=100.,
nbands=None,
kcommsize=None,
gauss_legendre=None,
frequency_cut=None,
frequency_scale=None,
direction=2):
self.initialize_calculation(None,
ecut,
nbands,
kcommsize,
gauss_legendre,
frequency_cut,
frequency_scale)
d = direction
d_pro = []
for i in range(3):
if i != d:
d_pro.append(i)
dummy = DF(calc=self.calc,
eta=0.0,
w=self.w * 1j,
ecut=self.ecut,
hilbert_trans=False)
dummy.txt = devnull
dummy.initialize(simple_version=True)
npw = dummy.npw
del dummy
q = [0.,0.,0.]
q[d] = 1.e-5
if self.nbands is None:
nbands = npw
else:
nbands = self.nbands
if self.txt is sys.stdout:
txt = 'response.txt'
else:
txt='response_'+self.txt.name
df = DF(calc=self.calc,
xc=None,
nbands=nbands,
eta=0.0,
q=q,
txt=txt,
vcut=self.vcut,
w=self.w * 1j,
ecut=self.ecut,
comm=world,
optical_limit=True,
G_plus_q=True,
kcommsize=self.kcommsize,
hilbert_trans=False)
print('Calculating RPA response function', file=self.txt)
print('Polarization: %s' % d, file=self.txt)
chi_wGG = df.get_chi(xc='RPA')
chi0_wGG = df.chi0_wGG
Nw_local = len(chi_wGG)
local_a0_w = np.zeros(Nw_local, dtype=complex)
a0_w = np.empty(len(self.w), complex)
local_a_w = np.zeros(Nw_local, dtype=complex)
a_w = np.empty(len(self.w), complex)
Gvec_Gv = np.dot(df.Gvec_Gc + np.array(q), df.bcell_cv)
gd = self.calc.density.gd
n_d = gd.get_size_of_global_array()[d]
d_d = gd.get_grid_spacings()[d]
r_d = np.array([i*d_d for i in range(n_d)])
print('Calculating real space integrals', file=self.txt)
int_G = np.zeros(npw, complex)
for iG in range(npw):
if df.Gvec_Gc[iG, d_pro[0]] == 0 and df.Gvec_Gc[iG, d_pro[1]] == 0:
int_G[iG] = np.sum(r_d * np.exp(1j*Gvec_Gv[iG, d] * r_d))*d_d
int2_GG = np.outer(int_G, int_G.conj())
print('Calculating dynamic polarizability', file=self.txt)
for i in range(Nw_local):
local_a0_w[i] = np.trace(np.dot(chi0_wGG[i], int2_GG))
local_a_w[i] = np.trace(np.dot(chi_wGG[i], int2_GG))
df.wcomm.all_gather(local_a0_w, a0_w)
df.wcomm.all_gather(local_a_w, a_w)
A = df.vol / gd.cell_cv[d,d]
a0_w *= A**2 / df.vol
a_w *= A**2 / df.vol
del df
C06 = np.sum(a0_w**2 * self.gauss_weights
* self.transform) * 3 / (2*np.pi)
C6 = np.sum(a_w**2 * self.gauss_weights
* self.transform) * 3 / (2*np.pi)
print('C06 = %s Ha*Bohr**6' % (C06.real / Ha), file=self.txt)
print('C6 = %s Ha*Bohr**6' % (C6.real / Ha), file=self.txt)
print(file=self.txt)
return C6.real / Ha, C06.real / Ha
def initialize_calculation(self,
w,
ecut,
nbands,
kcommsize,
gauss_legendre,
frequency_cut,
frequency_scale):
if kcommsize is None:
if len(self.calc.wfs.kd.bzk_kc) == 1:
kcommsize = 1
else:
kcommsize = world.size
if w is not None:
assert (gauss_legendre is None and
frequency_cut is None and
frequency_scale is None)
else:
if gauss_legendre is None:
gauss_legendre = 16
self.gauss_points, self.gauss_weights = p_roots(gauss_legendre)
if frequency_scale is None:
frequency_scale = 2.0
if frequency_cut is None:
frequency_cut = 800.
ys = 0.5 - 0.5 * self.gauss_points
ys = ys[::-1]
w = (-np.log(1-ys))**frequency_scale
w *= frequency_cut/w[-1]
alpha = (-np.log(1-ys[-1]))**frequency_scale/frequency_cut
transform = (-np.log(1-ys))**(frequency_scale-1) \
/ (1-ys)*frequency_scale/alpha
self.transform = transform
dummy = DF(calc=self.calc,
eta=0.0,
w=w * 1j,
q=[0.,0.,0.0001],
ecut=ecut,
optical_limit=True,
hilbert_trans=False,
kcommsize=kcommsize)
dummy.txt = devnull
dummy.initialize(simple_version=True)
self.npw = dummy.npw
self.ecut = ecut
self.w = w
self.gauss_legendre = gauss_legendre
self.frequency_cut = frequency_cut
self.frequency_scale = frequency_scale
self.kcommsize = kcommsize
self.nbands = nbands
print(file=self.txt)
print('Planewave cutoff : %s eV' % ecut, file=self.txt)
print('Number of Planewaves at Gamma : %s' % self.npw, file=self.txt)
if self.nbands is None:
print('Response function bands :'\
+ ' Equal to number of Planewaves', file=self.txt)
else:
print('Response function bands : %s' \
% self.nbands, file=self.txt)
print('Frequencies', file=self.txt)
if self.gauss_legendre is not None:
print(' Gauss-Legendre integration '\
+ 'with %s frequency points' % len(self.w), file=self.txt)
print(' Frequency cutoff is '\
+ '%s eV and scale (B) is %s' % (self.w[-1],
self.frequency_scale), file=self.txt)
else:
print(' %s specified frequency points' \
% len(self.w), file=self.txt)
print(' Frequency cutoff is %s eV' \
% self.w[-1], file=self.txt)
print(file=self.txt)
print('Parallelization scheme', file=self.txt)
print(' Total CPUs : %d' % dummy.comm.size, file=self.txt)
if dummy.kd.nbzkpts == 1:
print(' Band parsize : %d' % dummy.kcomm.size, file=self.txt)
else:
print(' Kpoint parsize : %d' % dummy.kcomm.size, file=self.txt)
print(' Frequency parsize : %d' % dummy.wScomm.size, file=self.txt)
print('Memory usage estimate', file=self.txt)
print(' chi0_wGG(Q) : %f M / cpu' \
% (dummy.Nw_local * self.npw**2 * 16. / 1024**2), file=self.txt)
print(file=self.txt)
del dummy
# view(atoms)
atoms.get_potential_energy()
calc.write('graphite.gpw', 'all')
if EELS:
f = paropen('graphite_q_list', 'w')
for i in range(1, 8):
w = np.linspace(0, 40, 401)
q = np.array([i / 20., 0., 0.]) # Gamma-M excitation
#q = np.array([i/20., -i/20., 0.]) # Gamma-K excitation
ecut = 40 + (i - 1) * 10
df = DF(calc='graphite.gpw',
nbands=nband,
q=q,
w=w,
eta=0.2,
ecut=ecut)
df.get_EELS_spectrum(filename='graphite_EELS_' + str(i))
df.check_sum_rule()
print(sqrt(np.inner(df.qq_v / Bohr, df.qq_v / Bohr)), ecut, file=f)
if rank == 0:
os.remove('graphite.gpw')
nc=[0,4],
coupling=True,
mode='RPA',
q=np.array([0.25, 0, 0]),
ecut=50.,
eta=0.2)
bse.get_dielectric_function('Al_bse.dat')
if df:
# Excited state calculation
q = np.array([1/4.,0.,0.])
w = np.linspace(0, 24, 241)
df = DF(calc='Al.gpw',
q=q,
w=w,
eta=0.2,
ecut=50,
hilbert_trans=False)
df.get_EELS_spectrum(filename='Al_df.dat')
df.write('Al.pckl')
df.check_sum_rule()
if check_spectrum:
d = np.loadtxt('Al_bse.dat')[:,2]
wpeak = 16.4
Nw = 164
if d[Nw] > d[Nw-1] and d[Nw] > d[Nw+1]:
pass
else:
raise ValueError('Plasmon peak not correct ! ')
def screened_interaction_kernel(self, iq):
"""Calcuate W_GG(w) for a given q.
if static: return W_GG(w=0)
is not static: return W_GG(q,w) - Vc_GG
"""
q = self.ibzq_qc[iq]
w = self.w_w.copy() * Hartree
optical_limit = False
if np.abs(q).sum() < 1e-8:
q = np.array([1e-12, 0,
0]) # arbitrary q, not really need to be calculated
optical_limit = True
hilbert_trans = True
if self.ppa or self.static:
hilbert_trans = False
df = DF(calc=self.calc,
q=q.copy(),
w=w,
nbands=self.nbands,
eshift=None,
optical_limit=optical_limit,
hilbert_trans=hilbert_trans,
xc='RPA',
time_ordered=True,
rpad=self.rpad,
vcut=self.vcut,
G_plus_q=True,
eta=self.eta * Hartree,
ecut=self.ecut.copy() * Hartree,
txt='df.out',
comm=self.dfcomm,
kcommsize=self.kcommsize)
df.initialize()
df.e_skn = self.e_skn.copy()
df.calculate()
dfinv_wGG = df.get_inverse_dielectric_matrix(xc='RPA')
assert df.ecut[0] == self.ecut[0]
if not self.static and not self.ppa:
assert df.eta == self.eta
assert df.Nw == self.Nw
assert df.dw == self.dw
# calculate Coulomb kernel and use truncation in 2D
delta_GG = np.eye(df.npw)
Vq_G = np.diag(
calculate_Kc(q,
df.Gvec_Gc,
self.acell_cv,
self.bcell_cv,
self.pbc,
integrate_gamma=True,
N_k=self.kd.N_c,
vcut=self.vcut))**0.5
if (self.vcut == '2D' and df.optical_limit) or self.numint:
for iG in range(len(df.Gvec_Gc)):
if df.Gvec_Gc[iG, 0] == 0 and df.Gvec_Gc[iG, 1] == 0:
v_q, v0_q = calculate_Kc_q(self.acell_cv,
self.bcell_cv,
self.pbc,
self.kd.N_c,
vcut=self.vcut,
q_qc=np.array([q]),
Gvec_c=df.Gvec_Gc[iG])
Vq_G[iG] = v_q[0]**0.5
self.Kc_GG = np.outer(Vq_G, Vq_G)
if self.ppa:
dfinv1_GG = dfinv_wGG[0] - delta_GG
dfinv2_GG = dfinv_wGG[1] - delta_GG
self.wt_GG = self.E0 * np.sqrt(dfinv2_GG / (dfinv1_GG - dfinv2_GG))
self.R_GG = -self.wt_GG / 2 * dfinv1_GG
del dfinv_wGG
dfinv_wGG = np.array([1j * pi * self.R_GG + delta_GG])
if self.static:
assert len(dfinv_wGG) == 1
W_GG = dfinv_wGG[0] * self.Kc_GG
return df, W_GG
else:
Nw = np.shape(dfinv_wGG)[0]
W_wGG = np.zeros_like(dfinv_wGG)
for iw in range(Nw):
dfinv_wGG[iw] -= delta_GG
W_wGG[iw] = dfinv_wGG[iw] * self.Kc_GG
return df, W_wGG
def screened_interaction_kernel(self, iq):
"""Calcuate W_GG(w) for a given q.
if static: return W_GG(w=0)
is not static: return W_GG(q,w) - Vc_GG
"""
q = self.ibzq_qc[iq]
w = self.w_w.copy()*Hartree
optical_limit = False
if np.abs(q).sum() < 1e-8:
q = np.array([1e-12, 0, 0]) # arbitrary q, not really need to be calculated
optical_limit = True
hilbert_trans = True
if self.ppa or self.static:
hilbert_trans = False
df = DF(calc=self.calc, q=q.copy(), w=w, nbands=self.nbands, eshift=None,
optical_limit=optical_limit, hilbert_trans=hilbert_trans, xc='RPA', time_ordered=True,
rpad=self.rpad, vcut=self.vcut, G_plus_q=True,
eta=self.eta*Hartree, ecut=self.ecut.copy()*Hartree,
txt='df.out', comm=self.dfcomm, kcommsize=self.kcommsize)
df.initialize()
df.e_skn = self.e_skn.copy()
df.calculate()
dfinv_wGG = df.get_inverse_dielectric_matrix(xc='RPA')
assert df.ecut[0] == self.ecut[0]
if not self.static and not self.ppa:
assert df.eta == self.eta
assert df.Nw == self.Nw
assert df.dw == self.dw
# calculate Coulomb kernel and use truncation in 2D
delta_GG = np.eye(df.npw)
Vq_G = np.diag(calculate_Kc(q,
df.Gvec_Gc,
self.acell_cv,
self.bcell_cv,
self.pbc,
integrate_gamma=True,
N_k=self.kd.N_c,
vcut=self.vcut))**0.5
if (self.vcut == '2D' and df.optical_limit) or self.numint:
for iG in range(len(df.Gvec_Gc)):
if df.Gvec_Gc[iG, 0] == 0 and df.Gvec_Gc[iG, 1] == 0:
v_q, v0_q = calculate_Kc_q(self.acell_cv,
self.bcell_cv,
self.pbc,
self.kd.N_c,
vcut=self.vcut,
q_qc=np.array([q]),
Gvec_c=df.Gvec_Gc[iG])
Vq_G[iG] = v_q[0]**0.5
self.Kc_GG = np.outer(Vq_G, Vq_G)
if self.ppa:
dfinv1_GG = dfinv_wGG[0] - delta_GG
dfinv2_GG = dfinv_wGG[1] - delta_GG
self.wt_GG = self.E0 * np.sqrt(dfinv2_GG / (dfinv1_GG - dfinv2_GG))
self.R_GG = - self.wt_GG / 2 * dfinv1_GG
del dfinv_wGG
dfinv_wGG = np.array([1j*pi*self.R_GG + delta_GG])
if self.static:
assert len(dfinv_wGG) == 1
W_GG = dfinv_wGG[0] * self.Kc_GG
return df, W_GG
else:
Nw = np.shape(dfinv_wGG)[0]
W_wGG = np.zeros_like(dfinv_wGG)
for iw in range(Nw):
dfinv_wGG[iw] -= delta_GG
W_wGG[iw] = dfinv_wGG[iw] * self.Kc_GG
return df, W_wGG
occupations=FermiDirac(0.001),
convergence={'bands': 70})
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write('si.gpw', 'all')
if ABS:
w = np.linspace(0, 24, 481)
q = np.array([0.0, 0.00001, 0.])
# getting macroscopic constant
df = DF(calc='si.gpw',
q=q,
w=(0., ),
eta=0.001,
hilbert_trans=False,
ecut=150,
optical_limit=True,
txt='df_1.out')
eM1, eM2 = df.get_macroscopic_dielectric_constant()
df.write('df_1.pckl')
if np.abs(eM1 - 13.991793) > 1e-3 or np.abs(eM2 - 12.589129) > 1e-3:
print(eM1, eM2)
raise ValueError('Please check dielectric constant !')
#getting absorption spectrum
df = DF(calc='si.gpw',
q=q,
def get_C6_coefficient(self,
ecut=100.,
nbands=None,
kcommsize=None,
gauss_legendre=None,
frequency_cut=None,
frequency_scale=None,
direction=2):
self.initialize_calculation(None, ecut, nbands, kcommsize,
gauss_legendre, frequency_cut,
frequency_scale)
d = direction
d_pro = []
for i in range(3):
if i != d:
d_pro.append(i)
dummy = DF(calc=self.calc,
eta=0.0,
w=self.w * 1j,
ecut=self.ecut,
hilbert_trans=False)
dummy.txt = devnull
dummy.initialize(simple_version=True)
npw = dummy.npw
del dummy
q = [0., 0., 0.]
q[d] = 1.e-5
if self.nbands is None:
nbands = npw
else:
nbands = self.nbands
if self.txt is sys.stdout:
txt = 'response.txt'
else:
txt = 'response_' + self.txt.name
df = DF(calc=self.calc,
xc=None,
nbands=nbands,
eta=0.0,
q=q,
txt=txt,
vcut=self.vcut,
w=self.w * 1j,
ecut=self.ecut,
comm=world,
optical_limit=True,
G_plus_q=True,
kcommsize=self.kcommsize,
hilbert_trans=False)
print('Calculating RPA response function', file=self.txt)
print('Polarization: %s' % d, file=self.txt)
chi_wGG = df.get_chi(xc='RPA')
chi0_wGG = df.chi0_wGG
Nw_local = len(chi_wGG)
local_a0_w = np.zeros(Nw_local, dtype=complex)
a0_w = np.empty(len(self.w), complex)
local_a_w = np.zeros(Nw_local, dtype=complex)
a_w = np.empty(len(self.w), complex)
Gvec_Gv = np.dot(df.Gvec_Gc + np.array(q), df.bcell_cv)
gd = self.calc.density.gd
n_d = gd.get_size_of_global_array()[d]
d_d = gd.get_grid_spacings()[d]
r_d = np.array([i * d_d for i in range(n_d)])
print('Calculating real space integrals', file=self.txt)
int_G = np.zeros(npw, complex)
for iG in range(npw):
if df.Gvec_Gc[iG, d_pro[0]] == 0 and df.Gvec_Gc[iG, d_pro[1]] == 0:
int_G[iG] = np.sum(
r_d * np.exp(1j * Gvec_Gv[iG, d] * r_d)) * d_d
int2_GG = np.outer(int_G, int_G.conj())
print('Calculating dynamic polarizability', file=self.txt)
for i in range(Nw_local):
local_a0_w[i] = np.trace(np.dot(chi0_wGG[i], int2_GG))
local_a_w[i] = np.trace(np.dot(chi_wGG[i], int2_GG))
df.wcomm.all_gather(local_a0_w, a0_w)
df.wcomm.all_gather(local_a_w, a_w)
A = df.vol / gd.cell_cv[d, d]
a0_w *= A**2 / df.vol
a_w *= A**2 / df.vol
del df
C06 = np.sum(
a0_w**2 * self.gauss_weights * self.transform) * 3 / (2 * np.pi)
C6 = np.sum(
a_w**2 * self.gauss_weights * self.transform) * 3 / (2 * np.pi)
print('C06 = %s Ha*Bohr**6' % (C06.real / Ha), file=self.txt)
print('C6 = %s Ha*Bohr**6' % (C6.real / Ha), file=self.txt)
print(file=self.txt)
return C6.real / Ha, C06.real / Ha
def initialize_calculation(self, w, ecut, nbands, kcommsize,
gauss_legendre, frequency_cut, frequency_scale):
if kcommsize is None:
if len(self.calc.wfs.kd.bzk_kc) == 1:
kcommsize = 1
else:
kcommsize = world.size
if w is not None:
assert (gauss_legendre is None and frequency_cut is None
and frequency_scale is None)
else:
if gauss_legendre is None:
gauss_legendre = 16
self.gauss_points, self.gauss_weights = p_roots(gauss_legendre)
if frequency_scale is None:
frequency_scale = 2.0
if frequency_cut is None:
frequency_cut = 800.
ys = 0.5 - 0.5 * self.gauss_points
ys = ys[::-1]
w = (-np.log(1 - ys))**frequency_scale
w *= frequency_cut / w[-1]
alpha = (-np.log(1 - ys[-1]))**frequency_scale / frequency_cut
transform = (-np.log(1-ys))**(frequency_scale-1) \
/ (1-ys)*frequency_scale/alpha
self.transform = transform
dummy = DF(calc=self.calc,
eta=0.0,
w=w * 1j,
q=[0., 0., 0.0001],
ecut=ecut,
optical_limit=True,
hilbert_trans=False,
kcommsize=kcommsize)
dummy.txt = devnull
dummy.initialize(simple_version=True)
self.npw = dummy.npw
self.ecut = ecut
self.w = w
self.gauss_legendre = gauss_legendre
self.frequency_cut = frequency_cut
self.frequency_scale = frequency_scale
self.kcommsize = kcommsize
self.nbands = nbands
print(file=self.txt)
print('Planewave cutoff : %s eV' % ecut, file=self.txt)
print('Number of Planewaves at Gamma : %s' % self.npw, file=self.txt)
if self.nbands is None:
print('Response function bands :'\
+ ' Equal to number of Planewaves', file=self.txt)
else:
print('Response function bands : %s' \
% self.nbands, file=self.txt)
print('Frequencies', file=self.txt)
if self.gauss_legendre is not None:
print(' Gauss-Legendre integration '\
+ 'with %s frequency points' % len(self.w), file=self.txt)
print(' Frequency cutoff is '\
+ '%s eV and scale (B) is %s' % (self.w[-1],
self.frequency_scale), file=self.txt)
else:
print(' %s specified frequency points' \
% len(self.w), file=self.txt)
print(' Frequency cutoff is %s eV' \
% self.w[-1], file=self.txt)
print(file=self.txt)
print('Parallelization scheme', file=self.txt)
print(' Total CPUs : %d' % dummy.comm.size, file=self.txt)
if dummy.kd.nbzkpts == 1:
print(' Band parsize : %d' % dummy.kcomm.size,
file=self.txt)
else:
print(' Kpoint parsize : %d' % dummy.kcomm.size,
file=self.txt)
print(' Frequency parsize : %d' % dummy.wScomm.size, file=self.txt)
print('Memory usage estimate', file=self.txt)
print(' chi0_wGG(Q) : %f M / cpu' \
% (dummy.Nw_local * self.npw**2 * 16. / 1024**2), file=self.txt)
print(file=self.txt)
del dummy
def E_q(self, q, index=None, direction=0, integrated=True):
if abs(np.dot(q, q))**0.5 < 1.e-5:
q = [0., 0., 0.]
q[direction] = 1.e-5
optical_limit = True
else:
optical_limit = False
dummy = DF(calc=self.calc,
eta=0.0,
w=self.w * 1j,
q=q,
ecut=self.ecut,
G_plus_q=True,
optical_limit=optical_limit,
hilbert_trans=False)
dummy.txt = devnull
dummy.initialize(simple_version=True)
npw = dummy.npw
del dummy
if self.nbands is None:
nbands = npw
else:
nbands = self.nbands
if self.txt is sys.stdout:
txt = 'response.txt'
else:
txt = 'response_' + self.txt.name
df = DF(calc=self.calc,
xc=None,
nbands=nbands,
eta=0.0,
q=q,
txt=txt,
vcut=self.vcut,
w=self.w * 1j,
ecut=self.ecut,
G_plus_q=True,
kcommsize=self.kcommsize,
comm=self.dfcomm,
optical_limit=optical_limit,
hilbert_trans=False)
if index is None:
print('Calculating KS response function at:', file=self.txt)
else:
print('#', index, \
'- Calculating KS response function at:', file=self.txt)
if optical_limit:
print('q = [0 0 0] -', 'Polarization: ', direction, file=self.txt)
else:
print('q = [%1.6f %1.6f %1.6f] -' \
% (q[0],q[1],q[2]), '%s planewaves' % npw, file=self.txt)
e_wGG = df.get_dielectric_matrix(xc='RPA', overwritechi0=True)
df.chi0_wGG = None
Nw_local = len(e_wGG)
local_E_q_w = np.zeros(Nw_local, dtype=complex)
E_q_w = np.empty(len(self.w), complex)
for i in range(Nw_local):
local_E_q_w[i] = (np.log(np.linalg.det(e_wGG[i])) + len(e_wGG[0]) -
np.trace(e_wGG[i]))
#local_E_q_w[i] = (np.sum(np.log(np.linalg.eigvals(e_wGG[i])))
# + len(e_wGG[0]) - np.trace(e_wGG[i]))
df.wcomm.all_gather(local_E_q_w, E_q_w)
del df
if self.gauss_legendre is not None:
E_q = np.sum(E_q_w * self.gauss_weights * self.transform) \
/ (4*np.pi)
else:
dws = self.w[1:] - self.w[:-1]
E_q = np.dot((E_q_w[:-1] + E_q_w[1:]) / 2., dws) / (2. * np.pi)
print('E_c(q) = %s eV' % E_q.real, file=self.txt)
print(file=self.txt)
if integrated:
return E_q.real
else:
return E_q_w.real
atoms.center()
calc = GPAW(gpts=(12,12,12),
eigensolver=RMM_DIIS(),
mixer=Mixer(0.1,3),
kpts=(4,4,4),
xc='LDA')
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write('Al1.gpw','all')
# Excited state calculation
q = np.array([1./4.,0.,0.])
w = np.linspace(0, 24, 241)
df = DF(calc='Al1.gpw', q=q, w=w, eta=0.2, ecut=50)
#df.write('Al.pckl')
df.get_EELS_spectrum(filename='EELS_Al_1')
atoms = Atoms('Al8',scaled_positions=[(0,0,0),
(0.5,0,0),
(0,0.5,0),
(0,0,0.5),
(0.5,0.5,0),
(0.5,0,0.5),
(0.,0.5,0.5),
(0.5,0.5,0.5)],
cell=[(0,a,a),(a,0,a),(a,a,0)],
pbc=True)
calc = GPAW(gpts=(24,24,24),
atoms.center()
calc = GPAW(gpts=(12,12,12),
eigensolver=RMM_DIIS(),
mixer=Mixer(0.1,3),
kpts=(4,4,4),
xc='LDA')
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write('Al1.gpw','all')
# Excited state calculation
q = np.array([1./4.,0.,0.])
w = np.linspace(0, 24, 241)
df = DF(calc='Al1.gpw', q=q, w=w, eta=0.2, ecut=50)
#df.write('Al.pckl')
df.get_EELS_spectrum(filename='EELS_Al_1')
atoms = Atoms('Al8',scaled_positions=[(0,0,0),
(0.5,0,0),
(0,0.5,0),
(0,0,0.5),
(0.5,0.5,0),
(0.5,0,0.5),
(0.,0.5,0.5),
(0.5,0.5,0.5)],
cell=[(0,a,a),(a,0,a),(a,a,0)],
pbc=True)
calc = GPAW(gpts=(24,24,24),
def get_chiM_2D_from_old_DF(filenames_eps, read, qpoints, d=None,
write_chi0 = False, name = None):
#rec_cell = reciprocal_cell*Bohr
#q_points = np.loadtxt(filename_qpoints)
#q_points = np.dot(q_points,rec_cell)
#Gvec = pickle.load(open(filename_Gvec %0))
#Gvec = np.dot(Gvec,rec_cell) # the cell has to be in bohr
from gpaw.response.df0 import DF
df = DF()
df.read(read + str(qpoints[0]))
cell = df.acell_cv
Gvec = np.dot(df.Gvec_Gc,df.bcell_cv)
nq = len(filenames_eps)#len(q_points[:,0])
L = cell[2,2] # Length of cell in Bohr
d /= Bohr # d in Bohr
z0 = L/2. # position of layer
npw = Gvec.shape[0]
nw = df.Nw
omega_w = df.w_w#[0.]
q_points_abs = []
Glist = []
for iG in range(npw): # List of G with Gx,Gy = 0
if Gvec[iG, 0] == 0 and Gvec[iG, 1] == 0:
Glist.append(iG)
epsM_2D_qw = np.zeros([nq, nw], dtype=complex)
epsD_2D_qw = np.zeros([nq, nw], dtype=complex)
chiM_2D_qw = np.zeros([nq, nw], dtype=complex)
chiD_2D_qw = np.zeros([nq, nw], dtype=complex)
VM_eff_qw = np.zeros([nq, nw], dtype=complex)
for iq in range(nq):
df.read(read + str(qpoints[iq]))
la,la,la,eps_wGG, chi_wGG = pickle.load(open(filenames_eps[iq]))
#chi_wGG = pickle.load(open(filenames_chi %iq))
#chi_wGG = np.array(chi_wGG)
eps_inv_wGG = np.zeros_like(eps_wGG, dtype = complex)
for iw in range(nw):
eps_inv_wGG[iw] = np.linalg.inv(eps_wGG[iw])
eps_inv_wGG[iw] = np.identity(npw)
del eps_wGG
q = df.q_c#q_points[iq]
q_abs = np.linalg.norm(q)
q_points_abs.append(q_abs) # return q in Ang
epsM_2D_inv = eps_inv_wGG[:, 0, 0]
epsD_2D_inv = np.zeros_like(eps_inv_wGG[:, 0, 0], dtype = complex)
chiM_2D = np.zeros_like(eps_inv_wGG[:, 0, 0], dtype = complex) #chi_wGG[:, 0, 0]#
chiD_2D = np.zeros_like(eps_inv_wGG[:, 0, 0], dtype = complex)
for iG in Glist[1:]:
G_z = Gvec[iG, 2]
epsM_2D_inv += 2./d * np.exp(1j*G_z*z0) * np.sin(G_z*d/2.) / G_z * eps_inv_wGG[:, iG, 0]
for iG1 in Glist[1:]:
G_z1 = Gvec[iG1, 2]
# intregrate over entire cell for z and z'
factor1 = z_factor(z0, L, G_z)
factor2 = z_factor(z0, L, G_z1, sign=-1)
chiD_2D += 1./L * factor1 * factor2 * chi_wGG[:, iG, iG1]
# intregrate z over d for epsilon^-1
#factor1 = z_factor2(z0, d, G_z)
#epsD_2D_inv += 2j / d / L * factor1 * factor2 * eps_inv_wGG[:, iG, iG1] #average
#epsD_2D_inv += 1j * G_z * np.exp(1j*G_z*z0) * factor2 * eps_inv_wGG[:, iG, iG1] #atz0
factor1 = z_factor(z0, d, G_z)
epsD_2D_inv += 12. / d**3 / L * factor1 * factor2 * eps_inv_wGG[:, iG, iG1] #kristian
epsM_2D_qw[iq, :] = 1. / epsM_2D_inv
epsD_2D_qw[iq, :] = 1. / epsD_2D_inv
chiM_2D_qw[iq, :] = L * chi_wGG[:, 0, 0] #chiM_2D#
chiD_2D_qw[iq, :] = chiD_2D
del chi_wGG, eps_inv_wGG
# Effective Coulomb interaction in 2D from eps_{2D}^{-1} = 1 + V_{eff} \chi_{2D}
VM_eff_qw = (1. /epsM_2D_qw - 1) / chiM_2D_qw
VD_eff_qw = (1. /epsD_2D_qw - 1) / chiD_2D_qw
chi0M_2D_qw = (1 - epsM_2D_qw) * 1. / VM_eff_qw # Chi0 from effective Coulomb
chi0D_2D_qw = (1 - epsD_2D_qw) * 1. / VD_eff_qw
pickle.dump((np.array(q_points_abs), omega_w, VM_eff_qw, VD_eff_qw,
chiM_2D_qw, chiD_2D_qw), open(name + '-chi.pckl', 'w'))
pickle.dump((np.array(q_points_abs), omega_w, VM_eff_qw, VD_eff_qw,
chi0M_2D_qw, chi0D_2D_qw, chiM_2D_qw, chiD_2D_qw,
epsM_2D_qw, epsD_2D_qw), open(name + '-2D.pckl', 'w'))
return np.array(q_points_abs), omega_w, chiM_2D_qw, chiD_2D_qw, VM_eff_qw, VD_eff_qw, epsM_2D_qw, epsD_2D_qw
w=np.linspace(0,20,201),
mode='RPA',
nc=[0,8],
nv=[0,8],
coupling=True,
q=np.array([0.0001,0,0.]),
optical_limit=True,
ecut=50.,
nbands=8)
bse.get_dielectric_function('C_bse.dat')
if df:
from gpaw.response.df0 import DF
df = DF('C_kpt8.gpw',
w=np.linspace(0,20,201),
q=np.array([0.0001,0,0.]),
optical_limit=True,
ecut=50.,
hilbert_trans=False)
df.get_absorption_spectrum(filename='C.dat')
if check_spectrum:
d = np.loadtxt('C_bse.dat')[:,2]
Nw1 = 96
Nw2 = 109
if d[Nw1] > d[Nw1-1] and d[Nw1] > d[Nw1+1] and \
d[Nw2] > d[Nw2-1] and d[Nw2] > d[Nw2+1] :
pass
else:
print(d[Nw1], d[Nw2])
raise ValueError('Absorption peak not correct ! ')
w=np.linspace(0, 20, 201),
mode='RPA',
nc=[0, 8],
nv=[0, 8],
coupling=True,
q=np.array([0.0001, 0, 0.]),
optical_limit=True,
ecut=50.,
nbands=8)
bse.get_dielectric_function('C_bse.dat')
if df:
from gpaw.response.df0 import DF
df = DF('C_kpt8.gpw',
w=np.linspace(0, 20, 201),
q=np.array([0.0001, 0, 0.]),
optical_limit=True,
ecut=50.,
hilbert_trans=False)
df.get_absorption_spectrum(filename='C.dat')
if check_spectrum:
d = np.loadtxt('C_bse.dat')[:, 2]
Nw1 = 96
Nw2 = 109
if d[Nw1] > d[Nw1-1] and d[Nw1] > d[Nw1+1] and \
d[Nw2] > d[Nw2-1] and d[Nw2] > d[Nw2+1] :
pass
else:
print(d[Nw1], d[Nw2])
raise ValueError('Absorption peak not correct ! ')
eigensolver='cg',
occupations=FermiDirac(0.001),
convergence={'bands': nbands})
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write('C2.gpw', 'all')
# Macroscopic dielectric constant calculation
q = np.array([0.0, 0.00001, 0.])
w = np.linspace(0, 15, 150)
df = DF(calc='C2.gpw',
q=q,
w=(0., ),
eta=0.001,
nbands=nbands,
ecut=50,
hilbert_trans=False,
optical_limit=True)
eM1, eM2 = df.get_macroscopic_dielectric_constant(xc='ALDA')
if np.abs(eM2 - 7.914302) > 1e-3:
raise ValueError(
"Incorrect value for Diamond dielectric constant with ALDA Kernel %.4f"
% (eM2))
# Dielectric function
df = DF(calc='C2.gpw',
q=q,
w=w,
eta=0.40,
# view(atoms)
atoms.get_potential_energy()
calc.write('graphite.gpw','all')
if EELS:
f = paropen('graphite_q_list', 'w')
for i in range(1,8):
w = np.linspace(0, 40, 401)
q = np.array([i/20., 0., 0.]) # Gamma-M excitation
#q = np.array([i/20., -i/20., 0.]) # Gamma-K excitation
ecut = 40 + (i-1)*10
df = DF(calc='graphite.gpw', nbands=nband, q=q, w=w,
eta=0.2,ecut=ecut)
df.get_EELS_spectrum(filename='graphite_EELS_' + str(i))
df.check_sum_rule()
print >> f, sqrt(np.inner(df.qq_v / Bohr, df.qq_v / Bohr)), ecut
if rank == 0:
os.remove('graphite.gpw')
eigensolver='cg',
occupations=FermiDirac(0.001),
convergence={'bands':nbands})
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write('C2.gpw','all')
# Macroscopic dielectric constant calculation
q = np.array([0.0, 0.00001, 0.])
w = np.linspace(0,15,150)
df = DF(calc='C2.gpw',
q=q,
w=(0.,),
eta=0.001,
nbands=nbands,
ecut=50,
hilbert_trans=False,
optical_limit=True)
eM1, eM2 = df.get_macroscopic_dielectric_constant(xc='ALDA')
if np.abs(eM2 - 7.914302) > 1e-3:
raise ValueError("Incorrect value for Diamond dielectric constant with ALDA Kernel %.4f" % (eM2))
# Dielectric function
df = DF(calc='C2.gpw',
q=q,
w=w,
eta=0.40,
nbands=nbands,
xc='Bootstrap',
nbands=nband+5,
parallel={'domain':1,
'band':1},
convergence={'bands':nband},
eigensolver = 'cg',
width=0.1)
atoms.set_calculator(calc)
atoms.get_potential_energy()
if EELS:
for i in range(1, 2):
w = np.linspace(0, 15, 301)
q = np.array([-i/64., i/64., 0.]) # Gamma - K
ecut = 40 + i*10
df = DF(calc=calc, q=q, w=w, eta=0.05, ecut = ecut,
txt='df_' + str(i) + '.out')
df.get_surface_response_function(z0=21.2/2, filename='be_EELS')
df.get_EELS_spectrum()
df.check_sum_rule()
df.write('df_' + str(i) + '.pckl')
if check:
d = np.loadtxt('be_EELS')
wpeak1 = 2.50 # eV
wpeak2 = 9.95
Nw1 = 50
Nw2 = 199
if (d[Nw1, 1] > d[Nw1-1, 1] and d[Nw1, 1] > d[Nw1+1, 1] and
d[Nw2, 1] > d[Nw2-1, 1] and d[Nw2, 1] > d[Nw2+1, 1]):
nc=[0,4],
coupling=True,
mode='RPA',
q=np.array([0.25, 0, 0]),
ecut=50.,
eta=0.2)
bse.get_dielectric_function('Al_bse.dat')
if df:
# Excited state calculation
q = np.array([1/4.,0.,0.])
w = np.linspace(0, 24, 241)
df = DF(calc='Al.gpw',
q=q,
w=w,
eta=0.2,
ecut=50,
hilbert_trans=False)
df.get_EELS_spectrum(filename='Al_df.dat')
df.write('Al.pckl')
df.check_sum_rule()
if check_spectrum:
d = np.loadtxt('Al_bse.dat')[:,2]
wpeak = 16.4
Nw = 164
if d[Nw] > d[Nw-1] and d[Nw] > d[Nw+1]:
pass
else:
raise ValueError('Plasmon peak not correct ! ')
def E_q(self,
q,
index=None,
direction=0,
integrated=True):
if abs(np.dot(q, q))**0.5 < 1.e-5:
q = [0.,0.,0.]
q[direction] = 1.e-5
optical_limit = True
else:
optical_limit = False
dummy = DF(calc=self.calc,
eta=0.0,
w=self.w * 1j,
q=q,
ecut=self.ecut,
G_plus_q=True,
optical_limit=optical_limit,
hilbert_trans=False)
dummy.txt = devnull
dummy.initialize(simple_version=True)
npw = dummy.npw
del dummy
if self.nbands is None:
nbands = npw
else:
nbands = self.nbands
if self.txt is sys.stdout:
txt = 'response.txt'
else:
txt='response_'+self.txt.name
df = DF(calc=self.calc,
xc=None,
nbands=nbands,
eta=0.0,
q=q,
txt=txt,
vcut=self.vcut,
w=self.w * 1j,
ecut=self.ecut,
G_plus_q=True,
kcommsize=self.kcommsize,
comm=self.dfcomm,
optical_limit=optical_limit,
hilbert_trans=False)
if index is None:
print('Calculating KS response function at:', file=self.txt)
else:
print('#', index, \
'- Calculating KS response function at:', file=self.txt)
if optical_limit:
print('q = [0 0 0] -', 'Polarization: ', direction, file=self.txt)
else:
print('q = [%1.6f %1.6f %1.6f] -' \
% (q[0],q[1],q[2]), '%s planewaves' % npw, file=self.txt)
e_wGG = df.get_dielectric_matrix(xc='RPA', overwritechi0=True)
df.chi0_wGG = None
Nw_local = len(e_wGG)
local_E_q_w = np.zeros(Nw_local, dtype=complex)
E_q_w = np.empty(len(self.w), complex)
for i in range(Nw_local):
local_E_q_w[i] = (np.log(np.linalg.det(e_wGG[i]))
+ len(e_wGG[0]) - np.trace(e_wGG[i]))
#local_E_q_w[i] = (np.sum(np.log(np.linalg.eigvals(e_wGG[i])))
# + len(e_wGG[0]) - np.trace(e_wGG[i]))
df.wcomm.all_gather(local_E_q_w, E_q_w)
del df
if self.gauss_legendre is not None:
E_q = np.sum(E_q_w * self.gauss_weights * self.transform) \
/ (4*np.pi)
else:
dws = self.w[1:] - self.w[:-1]
E_q = np.dot((E_q_w[:-1] + E_q_w[1:])/2., dws) / (2.*np.pi)
print('E_c(q) = %s eV' % E_q.real, file=self.txt)
print(file=self.txt)
if integrated:
return E_q.real
else:
return E_q_w.real
},
convergence={'bands': nband},
eigensolver='cg',
width=0.1)
atoms.set_calculator(calc)
atoms.get_potential_energy()
if EELS:
for i in range(1, 2):
w = np.linspace(0, 15, 301)
q = np.array([-i / 64., i / 64., 0.]) # Gamma - K
ecut = 40 + i * 10
df = DF(calc=calc,
q=q,
w=w,
eta=0.05,
ecut=ecut,
txt='df_' + str(i) + '.out')
df.get_surface_response_function(z0=21.2 / 2, filename='be_EELS')
df.get_EELS_spectrum()
df.check_sum_rule()
df.write('df_' + str(i) + '.pckl')
if check:
d = np.loadtxt('be_EELS')
wpeak1 = 2.50 # eV
wpeak2 = 9.95
Nw1 = 50
Nw2 = 199
def get_chiM_2D_from_old_DF(filenames_eps,
read,
qpoints,
d=None,
write_chi0=False,
name=None):
#rec_cell = reciprocal_cell*Bohr
#q_points = np.loadtxt(filename_qpoints)
#q_points = np.dot(q_points,rec_cell)
#Gvec = pickle.load(open(filename_Gvec %0))
#Gvec = np.dot(Gvec,rec_cell) # the cell has to be in bohr
from gpaw.response.df0 import DF
df = DF()
df.read(read + str(qpoints[0]))
cell = df.acell_cv
Gvec = np.dot(df.Gvec_Gc, df.bcell_cv)
nq = len(filenames_eps) #len(q_points[:,0])
L = cell[2, 2] # Length of cell in Bohr
d /= Bohr # d in Bohr
z0 = L / 2. # position of layer
npw = Gvec.shape[0]
nw = df.Nw
omega_w = df.w_w #[0.]
q_points_abs = []
Glist = []
for iG in range(npw): # List of G with Gx,Gy = 0
if Gvec[iG, 0] == 0 and Gvec[iG, 1] == 0:
Glist.append(iG)
epsM_2D_qw = np.zeros([nq, nw], dtype=complex)
epsD_2D_qw = np.zeros([nq, nw], dtype=complex)
chiM_2D_qw = np.zeros([nq, nw], dtype=complex)
chiD_2D_qw = np.zeros([nq, nw], dtype=complex)
VM_eff_qw = np.zeros([nq, nw], dtype=complex)
for iq in range(nq):
df.read(read + str(qpoints[iq]))
la, la, la, eps_wGG, chi_wGG = pickle.load(open(filenames_eps[iq]))
#chi_wGG = pickle.load(open(filenames_chi %iq))
#chi_wGG = np.array(chi_wGG)
eps_inv_wGG = np.zeros_like(eps_wGG, dtype=complex)
for iw in range(nw):
eps_inv_wGG[iw] = np.linalg.inv(eps_wGG[iw])
eps_inv_wGG[iw] = np.identity(npw)
del eps_wGG
q = df.q_c #q_points[iq]
q_abs = np.linalg.norm(q)
q_points_abs.append(q_abs) # return q in Ang
epsM_2D_inv = eps_inv_wGG[:, 0, 0]
epsD_2D_inv = np.zeros_like(eps_inv_wGG[:, 0, 0], dtype=complex)
chiM_2D = np.zeros_like(eps_inv_wGG[:, 0, 0],
dtype=complex) #chi_wGG[:, 0, 0]#
chiD_2D = np.zeros_like(eps_inv_wGG[:, 0, 0], dtype=complex)
for iG in Glist[1:]:
G_z = Gvec[iG, 2]
epsM_2D_inv += 2. / d * np.exp(1j * G_z * z0) * np.sin(
G_z * d / 2.) / G_z * eps_inv_wGG[:, iG, 0]
for iG1 in Glist[1:]:
G_z1 = Gvec[iG1, 2]
# intregrate over entire cell for z and z'
factor1 = z_factor(z0, L, G_z)
factor2 = z_factor(z0, L, G_z1, sign=-1)
chiD_2D += 1. / L * factor1 * factor2 * chi_wGG[:, iG, iG1]
# intregrate z over d for epsilon^-1
#factor1 = z_factor2(z0, d, G_z)
#epsD_2D_inv += 2j / d / L * factor1 * factor2 * eps_inv_wGG[:, iG, iG1] #average
#epsD_2D_inv += 1j * G_z * np.exp(1j*G_z*z0) * factor2 * eps_inv_wGG[:, iG, iG1] #atz0
factor1 = z_factor(z0, d, G_z)
epsD_2D_inv += 12. / d**3 / L * factor1 * factor2 * eps_inv_wGG[:,
iG,
iG1] #kristian
epsM_2D_qw[iq, :] = 1. / epsM_2D_inv
epsD_2D_qw[iq, :] = 1. / epsD_2D_inv
chiM_2D_qw[iq, :] = L * chi_wGG[:, 0, 0] #chiM_2D#
chiD_2D_qw[iq, :] = chiD_2D
del chi_wGG, eps_inv_wGG
# Effective Coulomb interaction in 2D from eps_{2D}^{-1} = 1 + V_{eff} \chi_{2D}
VM_eff_qw = (1. / epsM_2D_qw - 1) / chiM_2D_qw
VD_eff_qw = (1. / epsD_2D_qw - 1) / chiD_2D_qw
chi0M_2D_qw = (1 -
epsM_2D_qw) * 1. / VM_eff_qw # Chi0 from effective Coulomb
chi0D_2D_qw = (1 - epsD_2D_qw) * 1. / VD_eff_qw
pickle.dump((np.array(q_points_abs), omega_w, VM_eff_qw, VD_eff_qw,
chiM_2D_qw, chiD_2D_qw), open(name + '-chi.pckl', 'w'))
pickle.dump(
(np.array(q_points_abs), omega_w, VM_eff_qw, VD_eff_qw, chi0M_2D_qw,
chi0D_2D_qw, chiM_2D_qw, chiD_2D_qw, epsM_2D_qw, epsD_2D_qw),
open(name + '-2D.pckl', 'w'))
return np.array(
q_points_abs
), omega_w, chiM_2D_qw, chiD_2D_qw, VM_eff_qw, VD_eff_qw, epsM_2D_qw, epsD_2D_qw
