def __init__(self, e_k, beta, H_int=None, gf_struct=None,
mu0=0., mu_max=10, mu_min=-10.):
if mpi.is_master_node():
print(self.logo())
gf_struct = fix_gf_struct_type(gf_struct)
self.mu = mu0
self.beta = beta
self.mu_max = mu_max
self.mu_min = mu_min
self.e_k = e_k.copy()
self.e_k_MF = e_k.copy()
self.n_k = len(self.e_k.mesh)
self.target_shape = self.e_k.target_shape
self.shape_ab = self.e_k.target_shape
self.shape_abcd = list(self.shape_ab) * 2
self.norb = self.target_shape[0]
self.triu_idxs = np.triu_indices(self.norb, k=1)
if mpi.is_master_node():
print('beta =', self.beta)
print('mu =', self.mu)
print('bands =', self.norb)
print('n_k =', len(self.e_k.mesh))
print('H_int =', H_int)
print()
if gf_struct is None:
assert( H_int is None ), \
'Error: gf_struct = None, but H_int is not None'
if H_int is not None:
assert( gf_struct is not None ), \
'Error: H_int = None, but gf_struct is not None'
fundamental_operators = fundamental_operators_from_gf_struct(gf_struct)
if mpi.is_master_node():
print('gf_struct =', gf_struct)
print('fundamental_operators =', fundamental_operators)
print()
assert( is_operator_composed_of_only_fundamental_operators(
H_int, fundamental_operators) ), \
'Error: H_int is incompatible with gf_struct and its fundamental_operators'
self.U_abcd = get_rpa_tensor(H_int, fundamental_operators)
else:
self.U_abcd = np.zeros(self.shape_abcd)
def make_calc():
# ------------------------------------------------------------------
# -- Read precomputed ED data
filename = "data_pomerol.tar.gz"
p = read_TarGZ_HDFArchive(filename)
# ------------------------------------------------------------------
# -- RPA tensor
from triqs_tprf.rpa_tensor import get_rpa_tensor
from triqs_tprf.rpa_tensor import fundamental_operators_from_gf_struct
fundamental_operators = fundamental_operators_from_gf_struct(p.gf_struct)
p.U_abcd = get_rpa_tensor(p.H_int, fundamental_operators)
# ------------------------------------------------------------------
# -- Generalized PH susceptibility
loc_bse = ParameterCollection()
loc_bse.chi_wnn = chi_from_gg2_PH(p.G_iw, p.G2_iw_ph)
loc_bse.chi0_wnn = chi0_from_gg2_PH(p.G_iw, p.G2_iw_ph)
loc_bse.gamma_wnn = inverse_PH(loc_bse.chi0_wnn) - inverse_PH(
loc_bse.chi_wnn)
loc_bse.chi_wnn_ref = inverse_PH(
inverse_PH(loc_bse.chi0_wnn) - loc_bse.gamma_wnn)
np.testing.assert_array_almost_equal(loc_bse.chi_wnn.data,
loc_bse.chi_wnn_ref.data)
loc_bse.chi0_w = trace_nn(loc_bse.chi0_wnn)
loc_bse.chi_w = trace_nn(loc_bse.chi_wnn)
# ------------------------------------------------------------------
# -- RPA, using BSE inverses and constant Gamma
loc_rpa = ParameterCollection()
loc_rpa.U_abcd = p.U_abcd
# -- Build constant gamma
loc_rpa.gamma_wnn = loc_bse.gamma_wnn.copy()
loc_rpa.gamma_wnn.data[:] = loc_rpa.U_abcd[None, None, None, ...]
# Nb! In the three frequency form $\Gamma \propto U/\beta^2$
loc_rpa.gamma_wnn.data[:] /= p.beta**2
loc_rpa.chi0_wnn = loc_bse.chi0_wnn
loc_rpa.chi0_w = loc_bse.chi0_w
# -- Solve RPA
loc_rpa.chi_wnn = inverse_PH(
inverse_PH(loc_rpa.chi0_wnn) - loc_rpa.gamma_wnn)
loc_rpa.chi_w = trace_nn(loc_rpa.chi_wnn)
# ------------------------------------------------------------------
# -- Bubble RPA on lattice
lat_rpa = ParameterCollection()
# -- Setup dummy lattice Green's function equal to local Green's function
bz = BrillouinZone(
BravaisLattice(units=np.eye(3), orbital_positions=[(0, 0, 0)]))
periodization_matrix = np.diag(np.array(list([1] * 3), dtype=np.int32))
kmesh = MeshBrillouinZone(bz, periodization_matrix)
wmesh = MeshImFreq(beta=p.beta, S='Fermion', n_max=p.nwf_gf)
lat_rpa.g_wk = Gf(mesh=MeshProduct(wmesh, kmesh),
target_shape=p.G_iw.target_shape)
lat_rpa.g_wk[:, Idx(0, 0, 0)] = p.G_iw
# -- chi0_wk bubble and chi_wk_rpa bubble RPA
from triqs_tprf.lattice_utils import imtime_bubble_chi0_wk
lat_rpa.chi0_wk = imtime_bubble_chi0_wk(lat_rpa.g_wk, nw=1)
from triqs_tprf.lattice import solve_rpa_PH
lat_rpa.chi_wk = solve_rpa_PH(lat_rpa.chi0_wk, p.U_abcd)
lat_rpa.chi0_w = lat_rpa.chi0_wk[:, Idx(0, 0, 0)]
lat_rpa.chi_w = lat_rpa.chi_wk[:, Idx(0, 0, 0)]
print '--> cf Tr[chi0] and chi0_wk'
print loc_rpa.chi0_w.data.reshape((4, 4)).real
print lat_rpa.chi0_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(loc_rpa.chi0_w.data,
lat_rpa.chi0_w.data,
decimal=2)
print 'ok!'
print '--> cf Tr[chi_rpa] and chi_wk_rpa'
print loc_rpa.chi_w.data.reshape((4, 4)).real
print lat_rpa.chi_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(loc_rpa.chi_w.data,
lat_rpa.chi_w.data,
decimal=2)
print 'ok!'
# ------------------------------------------------------------------
# -- Lattice BSE
lat_bse = ParameterCollection()
lat_bse.g_wk = lat_rpa.g_wk
from triqs_tprf.lattice import fourier_wk_to_wr
lat_bse.g_wr = fourier_wk_to_wr(lat_bse.g_wk)
from triqs_tprf.lattice import chi0r_from_gr_PH
lat_bse.chi0_wnr = chi0r_from_gr_PH(nw=1, nnu=p.nwf, gr=lat_bse.g_wr)
from triqs_tprf.lattice import chi0q_from_chi0r
lat_bse.chi0_wnk = chi0q_from_chi0r(lat_bse.chi0_wnr)
# -- Lattice BSE calc
from triqs_tprf.lattice import chiq_from_chi0q_and_gamma_PH
lat_bse.chi_kwnn = chiq_from_chi0q_and_gamma_PH(lat_bse.chi0_wnk,
loc_bse.gamma_wnn)
# -- Trace results
from triqs_tprf.lattice import chi0q_sum_nu_tail_corr_PH
from triqs_tprf.lattice import chi0q_sum_nu
lat_bse.chi0_wk_tail_corr = chi0q_sum_nu_tail_corr_PH(lat_bse.chi0_wnk)
lat_bse.chi0_wk = chi0q_sum_nu(lat_bse.chi0_wnk)
from triqs_tprf.lattice import chiq_sum_nu, chiq_sum_nu_q
lat_bse.chi_kw = chiq_sum_nu(lat_bse.chi_kwnn)
lat_bse.chi0_w_tail_corr = lat_bse.chi0_wk_tail_corr[:, Idx(0, 0, 0)]
lat_bse.chi0_w = lat_bse.chi0_wk[:, Idx(0, 0, 0)]
lat_bse.chi_w = lat_bse.chi_kw[Idx(0, 0, 0), :]
print '--> cf Tr[chi0_wnk] and chi0_wk'
print lat_bse.chi0_w_tail_corr.data.reshape((4, 4)).real
print lat_bse.chi0_w.data.reshape((4, 4)).real
print lat_rpa.chi0_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(lat_bse.chi0_w_tail_corr.data,
lat_rpa.chi0_w.data)
np.testing.assert_array_almost_equal(lat_bse.chi0_w.data,
lat_rpa.chi0_w.data,
decimal=2)
print 'ok!'
print '--> cf Tr[chi_kwnn] and chi_wk'
print lat_bse.chi_w.data.reshape((4, 4)).real
print loc_bse.chi_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(lat_bse.chi_w.data,
loc_bse.chi_w.data)
print 'ok!'
# ------------------------------------------------------------------
# -- Store to hdf5
filename = 'data_bse_rpa.h5'
with HDFArchive(filename, 'w') as res:
res['p'] = p
# ------------------------------------------------------------------
# -- RPA tensor
gf_struct = [[0, 2]]
from triqs.operators import n, c, c_dag, Operator, dagger
H_int = U * n(0, 0) * n(0, 1)
print('H_int =', H_int)
from triqs_tprf.rpa_tensor import get_rpa_tensor
from triqs_tprf.rpa_tensor import fundamental_operators_from_gf_struct
fundamental_operators = fundamental_operators_from_gf_struct(gf_struct)
print(fundamental_operators)
U_abcd = get_rpa_tensor(H_int, fundamental_operators)
print('U_abcd =\n', U_abcd.reshape((4, 4)))
# ------------------------------------------------------------------
# -- RPA
from triqs_tprf.lattice import solve_rpa_PH
chi_wk = solve_rpa_PH(chi00_wk, U_abcd)
print('chi_q0 =\n', chi_wk[Idx(0), Idx(0, 0, 0)].real.reshape((4, 4)))
Sz = 0.5 * np.diag([+1., -1.])
chi_SzSz_wk = chi_wk[0, 0, 0, 0].copy()
chi_SzSz_wk.data[:] = np.einsum('wkabcd,ab,cd->wk', chi_wk.data, Sz, Sz)
print('chi_SzSz_q0 =', chi_SzSz_wk[Idx(0), Idx(0, 0, 0)].real)
def __init__(self,
e_k,
beta,
H_int=None,
gf_struct=None,
mu0=0.,
mu_max=10,
mu_min=-10.):
"""
Parameters:
e_k : single-particle dispersion
beta : inverse temperature
H_int : Local interaction Hamiltonian
gf_struct : gf_struct fixing orbital order between e_k and H_int
mu0 : chemical potential
mu_min, mu_max : range for chemical potential search
"""
if mpi.is_master_node():
print self.logo()
self.mu = mu0
self.beta = beta
self.mu_max = mu_max
self.mu_min = mu_min
self.e_k = e_k.copy()
self.e_k_MF = e_k.copy()
self.n_k = len(self.e_k.mesh)
self.target_shape = self.e_k.target_shape
self.shape_ab = self.e_k.target_shape
self.shape_abcd = list(self.shape_ab) * 2
self.norb = self.target_shape[0]
self.triu_idxs = np.triu_indices(self.norb, k=1)
if mpi.is_master_node():
print 'H_int =', H_int
print 'beta =', self.beta
print 'mu =', self.mu
print 'bands =', self.norb
print 'n_k =', len(self.e_k.mesh)
print
if gf_struct is None:
assert( H_int is None ), \
'Error: gf_struct = None, but H_int is not None'
if H_int is not None:
assert( gf_struct is not None ), \
'Error: H_int = None, but gf_struct is not None'
fundamental_operators = fundamental_operators_from_gf_struct(
gf_struct)
self.U_abcd = get_rpa_tensor(H_int, fundamental_operators)
if mpi.is_master_node():
print 'gf_struct =', gf_struct
print 'fundamental_operators =', fundamental_operators
else:
self.U_abcd = np.zeros(self.shape_abcd)
def make_calc():
# ------------------------------------------------------------------
# -- Read precomputed ED data
filename = "bse_and_rpa_loc_vs_latt.tar.gz"
p = read_TarGZ_HDFArchive(filename)['p']
# ------------------------------------------------------------------
# -- RPA tensor
from triqs_tprf.rpa_tensor import get_rpa_tensor
from triqs_tprf.rpa_tensor import fundamental_operators_from_gf_struct
fundamental_operators = fundamental_operators_from_gf_struct(p.gf_struct)
p.U_abcd = get_rpa_tensor(p.H_int, fundamental_operators)
# ------------------------------------------------------------------
# -- Generalized PH susceptibility
loc_bse = ParameterCollection()
loc_bse.chi_wnn = chi_from_gg2_PH(p.G_iw, p.G2_iw_ph)
loc_bse.chi0_wnn = chi0_from_gg2_PH(p.G_iw, p.G2_iw_ph)
loc_bse.gamma_wnn = inverse_PH(loc_bse.chi0_wnn) - inverse_PH(
loc_bse.chi_wnn)
loc_bse.chi_wnn_ref = inverse_PH(
inverse_PH(loc_bse.chi0_wnn) - loc_bse.gamma_wnn)
np.testing.assert_array_almost_equal(loc_bse.chi_wnn.data,
loc_bse.chi_wnn_ref.data)
from triqs_tprf.bse import solve_local_bse
loc_bse.gamma_wnn_ref = solve_local_bse(loc_bse.chi0_wnn, loc_bse.chi_wnn)
np.testing.assert_array_almost_equal(loc_bse.gamma_wnn.data,
loc_bse.gamma_wnn_ref.data)
loc_bse.chi0_w = trace_nn(loc_bse.chi0_wnn)
loc_bse.chi_w = trace_nn(loc_bse.chi_wnn)
# ------------------------------------------------------------------
# -- RPA, using BSE inverses and constant Gamma
loc_rpa = ParameterCollection()
loc_rpa.chi0_wnn = loc_bse.chi0_wnn
loc_rpa.chi0_w = loc_bse.chi0_w
loc_rpa.U_abcd = p.U_abcd
# -- Build constant gamma
from triqs_tprf.rpa_tensor import get_gamma_rpa
loc_rpa.gamma_wnn = get_gamma_rpa(loc_rpa.chi0_wnn, loc_rpa.U_abcd)
# -- Solve RPA
loc_rpa.chi_wnn = inverse_PH(
inverse_PH(loc_rpa.chi0_wnn) - loc_rpa.gamma_wnn)
loc_rpa.chi_w = trace_nn(loc_rpa.chi_wnn)
# ------------------------------------------------------------------
# -- Bubble RPA on lattice
lat_rpa = ParameterCollection()
# -- Setup dummy lattice Green's function equal to local Green's function
bz = BrillouinZone(
BravaisLattice(units=np.eye(3), orbital_positions=[(0, 0, 0)]))
periodization_matrix = np.diag(np.array(list([1] * 3), dtype=np.int32))
kmesh = MeshBrillouinZone(bz, periodization_matrix)
wmesh = MeshImFreq(beta=p.beta, S='Fermion', n_max=p.nwf_gf)
lat_rpa.g_wk = Gf(mesh=MeshProduct(wmesh, kmesh),
target_shape=p.G_iw.target_shape)
lat_rpa.g_wk[:, Idx(0, 0, 0)] = p.G_iw
# -- chi0_wk bubble and chi_wk_rpa bubble RPA
from triqs_tprf.lattice_utils import imtime_bubble_chi0_wk
lat_rpa.chi0_wk = imtime_bubble_chi0_wk(lat_rpa.g_wk, nw=1)
from triqs_tprf.lattice import solve_rpa_PH
lat_rpa.chi_wk = solve_rpa_PH(lat_rpa.chi0_wk, p.U_abcd)
lat_rpa.chi0_w = lat_rpa.chi0_wk[:, Idx(0, 0, 0)]
lat_rpa.chi_w = lat_rpa.chi_wk[:, Idx(0, 0, 0)]
print '--> cf Tr[chi0] and chi0_wk'
print loc_rpa.chi0_w.data.reshape((4, 4)).real
print lat_rpa.chi0_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(loc_rpa.chi0_w.data,
lat_rpa.chi0_w.data,
decimal=2)
print 'ok!'
print '--> cf Tr[chi_rpa] and chi_wk_rpa'
print loc_rpa.chi_w.data.reshape((4, 4)).real
print lat_rpa.chi_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(loc_rpa.chi_w.data,
lat_rpa.chi_w.data,
decimal=2)
print 'ok!'
# ------------------------------------------------------------------
# -- Lattice BSE
lat_bse = ParameterCollection()
lat_bse.g_wk = lat_rpa.g_wk
lat_bse.mu = p.mu
lat_bse.e_k = Gf(mesh=kmesh, target_shape=p.G_iw.target_shape)
lat_bse.e_k[Idx(0, 0, 0)] = np.eye(2)
lat_bse.sigma_w = p.G_iw.copy()
lat_bse.sigma_w << iOmega_n + lat_bse.mu * np.eye(2) - lat_bse.e_k[Idx(
0, 0, 0)] - inverse(p.G_iw)
lat_bse.g_wk_ref = lat_bse.g_wk.copy()
lat_bse.g_wk_ref[:, Idx(0, 0, 0)] << inverse(iOmega_n +
lat_bse.mu * np.eye(2) -
lat_bse.e_k[Idx(0, 0, 0)] -
lat_bse.sigma_w)
np.testing.assert_array_almost_equal(lat_bse.g_wk.data,
lat_bse.g_wk_ref.data)
#for w in lat_bse.g_wk.mesh.components[0]:
# print w, lat_bse.g_wk[w, Idx(0,0,0)][0, 0]
from triqs_tprf.lattice import fourier_wk_to_wr
lat_bse.g_wr = fourier_wk_to_wr(lat_bse.g_wk)
from triqs_tprf.lattice import chi0r_from_gr_PH
lat_bse.chi0_wnr = chi0r_from_gr_PH(nw=1, nn=p.nwf, g_nr=lat_bse.g_wr)
from triqs_tprf.lattice import chi0q_from_chi0r
lat_bse.chi0_wnk = chi0q_from_chi0r(lat_bse.chi0_wnr)
#for n in lat_bse.chi0_wnk.mesh.components[1]:
# print n.value, lat_bse.chi0_wnk[Idx(0), n, Idx(0,0,0)][0,0,0,0]
# -- Lattice BSE calc
from triqs_tprf.lattice import chiq_from_chi0q_and_gamma_PH
lat_bse.chi_kwnn = chiq_from_chi0q_and_gamma_PH(lat_bse.chi0_wnk,
loc_bse.gamma_wnn)
# -- Lattice BSE calc with built in trace
from triqs_tprf.lattice import chiq_sum_nu_from_chi0q_and_gamma_PH
lat_bse.chi_kw_ref = chiq_sum_nu_from_chi0q_and_gamma_PH(
lat_bse.chi0_wnk, loc_bse.gamma_wnn)
# -- Lattice BSE calc with built in trace using g_wk
from triqs_tprf.lattice import chiq_sum_nu_from_g_wk_and_gamma_PH
lat_bse.chi_kw_tail_corr_ref = chiq_sum_nu_from_g_wk_and_gamma_PH(
lat_bse.g_wk, loc_bse.gamma_wnn)
# -- Trace results
from triqs_tprf.lattice import chi0q_sum_nu_tail_corr_PH
from triqs_tprf.lattice import chi0q_sum_nu
lat_bse.chi0_wk_tail_corr = chi0q_sum_nu_tail_corr_PH(lat_bse.chi0_wnk)
lat_bse.chi0_wk = chi0q_sum_nu(lat_bse.chi0_wnk)
from triqs_tprf.lattice import chiq_sum_nu, chiq_sum_nu_q
lat_bse.chi_kw = chiq_sum_nu(lat_bse.chi_kwnn)
np.testing.assert_array_almost_equal(lat_bse.chi_kw.data,
lat_bse.chi_kw_ref.data)
from triqs_tprf.bse import solve_lattice_bse
lat_bse.chi_kw_tail_corr, tmp = solve_lattice_bse(lat_bse.g_wk,
loc_bse.gamma_wnn)
from triqs_tprf.bse import solve_lattice_bse_e_k_sigma_w
lat_bse.chi_kw_tail_corr_new = solve_lattice_bse_e_k_sigma_w(
lat_bse.mu, lat_bse.e_k, lat_bse.sigma_w, loc_bse.gamma_wnn)
np.testing.assert_array_almost_equal(lat_bse.chi_kw_tail_corr.data,
lat_bse.chi_kw_tail_corr_ref.data)
np.testing.assert_array_almost_equal(lat_bse.chi_kw_tail_corr.data,
lat_bse.chi_kw_tail_corr_new.data)
np.testing.assert_array_almost_equal(lat_bse.chi_kw_tail_corr_ref.data,
lat_bse.chi_kw_tail_corr_new.data)
lat_bse.chi0_w_tail_corr = lat_bse.chi0_wk_tail_corr[:, Idx(0, 0, 0)]
lat_bse.chi0_w = lat_bse.chi0_wk[:, Idx(0, 0, 0)]
lat_bse.chi_w_tail_corr = lat_bse.chi_kw_tail_corr[Idx(0, 0, 0), :]
lat_bse.chi_w = lat_bse.chi_kw[Idx(0, 0, 0), :]
print '--> cf Tr[chi0_wnk] and chi0_wk'
print lat_bse.chi0_w_tail_corr.data.reshape((4, 4)).real
print lat_bse.chi0_w.data.reshape((4, 4)).real
print lat_rpa.chi0_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(lat_bse.chi0_w_tail_corr.data,
lat_rpa.chi0_w.data)
np.testing.assert_array_almost_equal(lat_bse.chi0_w.data,
lat_rpa.chi0_w.data,
decimal=2)
print 'ok!'
print '--> cf Tr[chi_kwnn] and chi_wk (without chi0 tail corr)'
print lat_bse.chi_w.data.reshape((4, 4)).real
print loc_bse.chi_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(lat_bse.chi_w.data,
loc_bse.chi_w.data)
print 'ok!'
# ------------------------------------------------------------------
# -- Use chi0 tail corrected trace to correct chi_rpa cf bubble
dchi_wk = lat_bse.chi0_wk_tail_corr - lat_bse.chi0_wk
dchi_w = dchi_wk[:, Idx(0, 0, 0)]
loc_rpa.chi_w_tail_corr = loc_rpa.chi_w + dchi_w
# -- this will be the same, but it will be close to the real physical value
lat_bse.chi_w_tail_corr_ref = lat_bse.chi_w + dchi_w
loc_bse.chi_w_tail_corr_ref = loc_bse.chi_w + dchi_w
print '--> cf Tr[chi_rpa] and chi_wk_rpa'
print loc_rpa.chi_w.data.reshape((4, 4)).real
print loc_rpa.chi_w_tail_corr.data.reshape((4, 4)).real
print lat_rpa.chi_w.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(loc_rpa.chi_w_tail_corr.data,
lat_rpa.chi_w.data,
decimal=3)
print '--> cf Tr[chi_kwnn] with tail corr (from chi0_wnk)'
print lat_bse.chi_w_tail_corr.data.reshape((4, 4)).real
print lat_bse.chi_w_tail_corr_ref.data.reshape((4, 4)).real
np.testing.assert_array_almost_equal(lat_bse.chi_w_tail_corr.data,
lat_bse.chi_w_tail_corr_ref.data)
print 'ok!'
# ------------------------------------------------------------------
# -- Store to hdf5
filename = 'data_bse_rpa.h5'
with HDFArchive(filename, 'w') as res:
res['p'] = p
spin_names = ['up', 'do']
orb_names = list(range(norb))
# TRIQS uses spin as slow index
gf_struct = [[spin_name, norb] for spin_name in spin_names]
Umat, Upmat = U_matrix_kanamori(n_orb=norb, U_int=U, J_hund=J)
H_int = h_int_kanamori(spin_names,
orb_names,
U=Umat,
Uprime=Upmat,
J_hund=J,
off_diag=True)
fundamental_operators = fundamental_operators_from_gf_struct(gf_struct)
U_abcd = get_rpa_tensor(H_int, fundamental_operators) # given in cc^+cc^+
# ----------------------------------------------------------------------
# -- Lattice susceptbility in the RPA approximation
from triqs_tprf.lattice import solve_rpa_PH
chi_wk = solve_rpa_PH(chi00_wk, U_abcd)
# ======================================================================
# Matrix RPA formalism
# ----------------------------------------------------------------------
# -- Showcase reshaping from 4-rank tensors to matrix as done in papers
# -- and test if the process is reversable
