def SIAM(U, e_f, V, D, beta, filename="qmc_results.h5"):
# Create hybridization function
Delta = V**2 * Flat(D)
# Construct the impurity solver with the inverse temperature
# and the structure of the Green's functions
S = Solver(beta=beta, gf_struct={'up': [0], 'down': [0]}, n_l=50)
# Initialize the non-interacting Green's function S.G0_iw
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n - e_f - Delta)
# Run the solver. The results will be in S.G_tau, S.G_iw and S.G_l
S.solve(
h_int=U * n('up', 0) * n('down', 0), # Local Hamiltonian
n_cycles=2000000, # Number of QMC cycles
length_cycle=50, # Length of one cycle
n_warmup_cycles=20000, # Warmup cycles
measure_g_l=
True, # Measure G_l (representation of G in terms of Legendre polynomials)
use_norm_as_weight=
True, # Necessary option for the measurement of the density matrix
measure_density_matrix=True, # Measure reduced impurity density matrix
measure_pert_order=True) # Measure histogram of k
# Save the results in an HDF5 file (only on the master node)
if mpi.is_master_node():
with HDFArchive(filename, 'w') as Results:
Results["G_tau"] = S.G_tau
Results["G_iw"] = S.G_iw
Results["G_l"] = S.G_l
Results["rho"] = S.density_matrix
Results["k_histogram"] = S.perturbation_order_total
Results["average_sign"] = S.average_sign
def SIAM(U, e_f, V, D, beta, filename="qmc_results.h5"):
Delta = V**2 * Flat(D)
N_MC = 1e5
l_max = 10
independent_samples = 16
for l in range(l_max + 1):
for i in range(independent_samples):
S = Solver(beta=beta, gf_struct={'up': [0], 'down': [0]})
# Initialize the non-interacting Green's function S.G0_iw
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n - e_f - Delta)
# Run the solver. The results will be in S.G_tau, S.G_iw and S.G_l
S.solve(
h_int=U * n('up', 0) * n('down', 0), # Local Hamiltonian
n_cycles=int(N_MC / 2**l), # Number of QMC cycles
length_cycle=2**l, # Length of one cycle
n_warmup_cycles=int(N_MC / 2**l / 100), # Warmup cycles
measure_g_tau=False, # Don't measure G_tau
measure_g_l=False, # Don't measure G_l
perform_post_proc=False, # Don't measure G_iw
use_norm_as_weight=
True, # Necessary option for the measurement of the density matrix
measure_density_matrix=
True, # Measure reduced impurity density matrix
random_seed=i * 8521 + l * 14187 +
mpi.rank * 7472) # Random seed, very important!
# Save the results in an HDF5 file (only on the master node)
if mpi.is_master_node():
with HDFArchive(filename) as Results:
Results["rho_l{}_i{}".format(l, i)] = S.density_matrix
def DMFT(U, e_d, t, beta, filename="dmft_results.h5"):
# Construct the CT-HYB-QMC solver
S = Solver(beta=beta, gf_struct={'up': [0], 'down': [0]}, n_l=50)
# Initialize Delta
Delta = GfImFreq(beta=beta, indices=[0])
Delta << t**2 * SemiCircular(half_bandwidth=2 * t)
# Now do the DMFT loop
n_iter = 8
for iter in range(n_iter):
# Compute new S.G0_iw
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n - e_d - Delta)
# Run the solver
S.solve(
h_int=U * n('up', 0) * n('down', 0), # Local Hamiltonian
n_cycles=200000, # Number of QMC cycles
length_cycle=50, # Length of a cycle
n_warmup_cycles=2000, # How many warmup cycles
measure_g_l=True)
# Compute new Delta with the self-consistency condition while imposing paramagnetism
g_l = (S.G_l['up'] + S.G_l['down']) / 2.
Delta.set_from_legendre(t**2 * g_l)
# Intermediate saves
if mpi.is_master_node():
with HDFArchive(filename) as Results:
Results["G_tau_iter{}".format(iter)] = S.G_tau
Results["G_iw_iter{}".format(iter)] = S.G_iw
Results["G_l_iter{}".format(iter)] = S.G_l
Results["Sigma_iter{}".format(iter)] = S.Sigma_iw
if mpi.is_master_node():
with HDFArchive(filename) as Results:
Results["G_tau"] = S.G_tau
Results["G_iw"] = S.G_iw
Results["G_l"] = S.G_l
Results["Sigma"] = S.Sigma_iw
from pytriqs.gf import *
from pytriqs.operators import *
from pytriqs.archive import *
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.cthyb import Solver
# Set up a few parameters
U = 2.5
half_bandwidth = 1.0
chemical_potential = U / 2.0
beta = 100
n_loops = 5
# Construct the CTQMC solver
S = Solver(beta=beta, gf_struct=[['up', [0]], ['down', [0]]])
# Initalize the Green's function to a semi circular DOS
S.G_iw << SemiCircular(half_bandwidth)
# Now do the DMFT loop
for i_loop in range(n_loops):
# Compute new S.G0_iw with the self-consistency condition while imposing paramagnetism
g = 0.5 * (S.G_iw['up'] + S.G_iw['down'])
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n + chemical_potential -
(half_bandwidth / 2.0)**2 * g)
# Run the solver
S.solve(
h_int=U * n('up', 0) * n('down', 0), # Local Hamiltonian
if p.offdiag: # off-diagonal solver, 1 block NBand x NBand
gf_struct = {'0': p.bands_T}
else: # diagonal solver, NBand blocks 1 x 1
gf_struct = {}
for band in p.bands_T:
gf_struct[band] = [0]
if mpi.is_master_node() and p.NBins == 1:
stars = '**********************************************************'
PrintAndWrite(
'\n' + stars + '\n* CT_HYB solver START, iteration ' + str(NLoop) +
'.\n' + stars + '\n', logfname)
S = Solver(beta=p.beta,
gf_struct=gf_struct,
n_iw=p.NMats,
n_l=p.NLeg,
n_tau=p.NTau)
if p.offdiag: # off-diagonal solver, 1 block NBand x NBand
for block, gw0 in S.G0_iw:
gw0 << GF
else: # diagonal solver, NBand blocks 1 x 1
for block, gw0 in S.G0_iw:
gw0 << GF[block, block]
# reading the Coulomb matrix from file ####################
U_M = sp.loadtxt('Umm.dat')
# model Hamiltonian and other operators ###################
h_int = Operator()
import os
if not os.path.exists('results_two_bands'):
os.makedirs('results_two_bands')
# Parameters of the model
t = 1.0
beta = 10.0
n_loops = 10
filling = 'half' # or 'quarter'
n_orbitals = 2
# Construct the solver
S = Solver(beta=beta,
gf_struct={
'up-0': [0],
'up-1': [0],
'down-0': [0],
'down-1': [0]
})
for coeff in [0.0, 0.1, 0.2]:
# Run for several values of U
for U in np.arange(1.0, 13.0, 1.0):
J = coeff * U
# Expression of mu for half and quarter filling
if filling == 'half':
mu = 0.5 * U + 0.5 * (U - 2 * J) + 0.5 * (U - 3 * J)
elif filling == 'quarter':
gf_struct = op.set_operator_structure(spin_names, orb_names, off_diag=off_diag)
# Construct the 4-index U matrix U_{ijkl}
# The spherically-symmetric U matrix is parametrised by the radial integrals
# F_0, F_2, F_4, which are related to U and J. We use the functions provided
# in the TRIQS library to construct this easily:
U_mat = op.U_matrix(l=l, U_int=U, J_hund=J, basis='spherical')
# Set the interacting part of the local Hamiltonian
# Here we use the full rotationally-invariant interaction parametrised
# by the 4-index tensor U_{ijkl}.
# The TRIQS library provides a function to build this Hamiltonian from the U tensor:
H = op.h_int_slater(spin_names, orb_names, U_mat, off_diag=off_diag)
# Construct the solver
S = Solver(beta=beta, gf_struct=gf_struct)
# Set the hybridization function and G0_iw for the Bethe lattice
delta_iw = GfImFreq(indices=[0], beta=beta)
delta_iw << (half_bandwidth / 2.0)**2 * SemiCircular(half_bandwidth)
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n + mu - delta_iw)
# Now start the DMFT loops
for i_loop in range(n_loop):
# Determine the new Weiss field G0_iw self-consistently
if i_loop > 0:
g_iw = GfImFreq(indices=[0], beta=beta)
# Impose paramagnetism
for name, g in S.G_tau:
from pytriqs.gf import *
from pytriqs.operators import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
from pytriqs.archive import HDFArchive
import pytriqs.utility.mpi as mpi
# Parameters
D, V, U = 1.0, 0.2, 4.0
e_f, beta = -U/2.0, 50
# Construct the impurity solver with the inverse temperature
# and the structure of the Green's functions
S = Solver(beta = beta, gf_struct = [ ('up',[0]), ('down',[0]) ], n_l = 100)
# Initialize the non-interacting Green's function S.G0_iw
for name, g0 in S.G0_iw: g0 << inverse(iOmega_n - e_f - V**2 * Wilson(D))
# Run the solver. The results will be in S.G_tau, S.G_iw and S.G_l
S.solve(h_int = U * n('up',0) * n('down',0), # Local Hamiltonian
n_cycles = 500000, # Number of QMC cycles
length_cycle = 200, # Length of one cycle
n_warmup_cycles = 10000, # Warmup cycles
measure_G_l = True) # Measure G_l
# Save the results in an HDF5 file (only on the master node)
if mpi.is_master_node():
with HDFArchive("aim_solution.h5",'w') as Results:
Results["G_tau"] = S.G_tau
Results["G_iw"] = S.G_iw
Results["G_l"] = S.G_l
from itertools import product
import numpy as np
import os
if not os.path.exists('results_two_bands'):
os.makedirs('results_two_bands')
# Parameters of the model
t = 1.0
beta = 10.0
n_loops = 10
filling = 'half' # or 'quarter'
n_orbitals = 2
# Construct the solver
S = Solver(beta = beta, gf_struct = {'up-0':[0], 'up-1':[0], 'down-0':[0], 'down-1':[0]})
for coeff in [0.0, 0.1, 0.2]:
# Run for several values of U
for U in np.arange(1.0, 13.0, 1.0):
J = coeff * U
# Expression of mu for half and quarter filling
if filling == 'half':
mu = 0.5*U + 0.5*(U-2*J) + 0.5*(U-3*J)
elif filling == 'quarter':
mu = -0.81 + (0.6899-1.1099*coeff)*U + (-0.02548+0.02709*coeff-0.1606*coeff**2)*U**2
# Set the interacting Kanamori hamiltonian
gf_struct = op.set_operator_structure(spin_names,orb_names,off_diag=off_diag)
# Construct the 4-index U matrix U_{ijkl}
# The spherically-symmetric U matrix is parametrised by the radial integrals
# F_0, F_2, F_4, which are related to U and J. We use the functions provided
# in the TRIQS library to construct this easily:
U_mat = op.U_matrix(l=l, U_int=U, J_hund=J, basis='spherical')
# Set the interacting part of the local Hamiltonian
# Here we use the full rotationally-invariant interaction parametrised
# by the 4-index tensor U_{ijkl}.
# The TRIQS library provides a function to build this Hamiltonian from the U tensor:
H = op.h_int_slater(spin_names,orb_names,U_mat,off_diag=off_diag)
# Construct the solver
S = Solver(beta=beta, gf_struct=gf_struct)
# Set the hybridization function and G0_iw for the Bethe lattice
delta_iw = GfImFreq(indices=[0], beta=beta)
delta_iw << (half_bandwidth/2.0)**2 * SemiCircular(half_bandwidth)
for name, g0 in S.G0_iw: g0 << inverse(iOmega_n + mu - delta_iw)
# Now start the DMFT loops
for i_loop in range(n_loop):
# Determine the new Weiss field G0_iw self-consistently
if i_loop > 0:
g_iw = GfImFreq(indices=[0], beta=beta)
# Impose paramagnetism
for name, g in S.G_tau: g_iw << g_iw + (0.5/n_orbs)*Fourier(g)
# Compute S.G0_iw with the self-consistency condition
from pytriqs.operators import *
from pytriqs.archive import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
import numpy as np
import os
if not os.path.exists('results_one_band'):
os.makedirs('results_one_band')
# Parameters of the model
t = 1.0
beta = 10.0
n_loops = 10
# Construct the impurity solver
S = Solver(beta = beta, gf_struct = {'up':[0], 'down':[0]})
# I run for several values of U
for U in np.arange(1.0, 13.0):
print 'U =', U
# This is a first guess for G
S.G_iw << SemiCircular(2*t)
# DMFT loop with self-consistency
for i in range(n_loops):
print "\n\nIteration = %i / %i" % (i+1, n_loops)
# Symmetrize the Green's function and use self-consistency
g = 0.5 * ( S.G_iw['up'] + S.G_iw['down'] )
from pytriqs.gf.local import *
from pytriqs.operators import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
# Parameters
D = 1.0 # Half-bandwidth of the bath
V = 0.2 # Hybridisation amplitude
U = 4.0 # Coulomb interaction
e_f = -U/2 # Local energy level
h = 0.01 # External field
beta = 50 # Inverse temperature
# Construct the impurity solver with the inverse temperature
# and the structure of the Green's functions
S = Solver(beta = beta, gf_struct = {'up':[0], 'down':[0]})
# Initialize the non-interacting Green's function S.G0_iw
# External magnetic field introduces Zeeman energy splitting between
# different spin components
S.G0_iw['up'] << inverse(iOmega_n - e_f + h/2 - V**2 * Wilson(D))
S.G0_iw['down'] << inverse(iOmega_n - e_f - h/2 - V**2 * Wilson(D))
# Run the solver
S.solve(h_int = U * n('up',0) * n('down',0), # Local Hamiltonian
n_cycles = 500000, # Number of QMC cycles
length_cycle = 200, # Length of one cycle
n_warmup_cycles = 10000, # Warmup cycles
measure_density_matrix = True, # Measure the reduced density matrix
use_norm_as_weight = True) # Required to measure the density matrix
# Extract accumulated density matrix
from pytriqs.gf.local import *
from pytriqs.operators import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
from pytriqs.archive import HDFArchive
import pytriqs.utility.mpi as mpi
# Parameters
D, V, U = 1.0, 0.2, 4.0
e_f, beta = -U/2.0, 50
# Construct the impurity solver with the inverse temperature
# and the structure of the Green's functions
S = Solver(beta = beta, gf_struct = {'up':[0], 'down':[0]}, n_l = 100)
# Initialize the non-interacting Green's function S.G0_iw
for name, g0 in S.G0_iw: g0 << inverse(iOmega_n - e_f - V**2 * Wilson(D))
# Run the solver. The results will be in S.G_tau, S.G_iw and S.G_l
S.solve(h_int = U * n('up',0) * n('down',0), # Local Hamiltonian
n_cycles = 500000, # Number of QMC cycles
length_cycle = 200, # Length of one cycle
n_warmup_cycles = 10000, # Warmup cycles
measure_g_l = True) # Measure G_l
# Save the results in an HDF5 file (only on the master node)
if mpi.is_master_node():
with HDFArchive("aim_solution.h5",'w') as Results:
Results["G_tau"] = S.G_tau
Results["G_iw"] = S.G_iw
Results["G_l"] = S.G_l
from pytriqs.operators import *
from pytriqs.archive import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
import numpy as np
import os
if not os.path.exists('results_one_band'):
os.makedirs('results_one_band')
# Parameters of the model
t = 1.0
beta = 10.0
n_loops = 10
# Construct the impurity solver
S = Solver(beta=beta, gf_struct={'up': [0], 'down': [0]})
# I run for several values of U
for U in np.arange(1.0, 13.0):
print 'U =', U
# This is a first guess for G
S.G_iw << SemiCircular(2 * t)
# DMFT loop with self-consistency
for i in range(n_loops):
print "\n\nIteration = %i / %i" % (i + 1, n_loops)
# Symmetrize the Green's function and use self-consistency
g = 0.5 * (S.G_iw['up'] + S.G_iw['down'])
#GF = ReadGW(NLoop,p.NBand,p.NMats,p.beta,p.bands_T,FitMin,FitMax,mpi.rank,p.offdiag,logfname)
#GF = FitTailGreen(GF,p.NBand,FitMin,FitMax,6)
GF = ReadGW2(NLoop,p.NBand,p.NMats,p.beta,p.bands_T,FitMin,FitMax,p.NFit,mpi.rank,p.offdiag,1,logfname)
if p.offdiag: # off-diagonal solver, 1 block NBand x NBand
gf_struct = { '0': p.bands_T }
else: # diagonal solver, NBand blocks 1 x 1
gf_struct = {}
for band in p.bands_T: gf_struct[band] = [0]
if mpi.is_master_node() and p.NBins == 1:
stars = '**********************************************************'
PrintAndWrite('\n'+stars+'\n* CT_HYB solver START, iteration '+str(NLoop)+'.\n'+stars+'\n',logfname)
S = Solver(beta = p.beta, gf_struct = gf_struct, n_iw = p.NMats, n_l = p.NLeg, n_tau = p.NTau)
if p.offdiag: # off-diagonal solver, 1 block NBand x NBand
for block, gw0 in S.G0_iw: gw0 << GF
else: # diagonal solver, NBand blocks 1 x 1
for block, gw0 in S.G0_iw: gw0 << GF[block,block]
# reading the Coulomb matrix from file ####################
U_M = sp.loadtxt('Umm.dat')
# model Hamiltonian and other operators ###################
h_int = Operator()
N_tot = Operator()
for b1 in range(p.NBand):
N_tot = N_tot + n('0',p.bands_T[b1])
from pytriqs.gf.local import *
from pytriqs.operators import *
from pytriqs.archive import HDFArchive
from pytriqs.applications.impurity_solvers.cthyb import Solver
import pytriqs.utility.mpi as mpi
D, V, U = 1.0, 0.2, 4.0
e_f, beta = -U/2.0, 50
# Construct the impurity solver
S = Solver(beta = beta, gf_struct = {'up':[0],'down':[0]})
# Loop for two random generators
for random_name in ['mt11213b','lagged_fibonacci607']:
for spin, g0 in S.G0_iw:
g0 << inverse(iOmega_n - e_f - V**2 * Wilson(D))
# Solve using random_name as a generator
S.solve(h_int = U * n('up',0) * n('down',0), # Local Hamiltonian
n_cycles = 100000, # Number of QMC cycles
length_cycle = 200, # Length of one cycle
n_warmup_cycles = 10000, # Number of warmup cycles
random_name = random_name) # Name of the random generator
# Save the results in an hdf5 file (only on the master node)
if mpi.is_master_node():
with HDFArchive("random.h5") as Results:
Results["G_iw%s"%(random_name)] = S.G_iw
# A mask giving the k points inside the central patch
in_central_patch = (np.abs(kx) < np.pi/np.sqrt(2)) & (np.abs(ky) < np.pi/np.sqrt(2))
# Find the partial densities of states associated to the patches
energies, epsilon, rho, delta = {},{}, {}, {}
energies['even'] = np.extract(in_central_patch, epsk)
energies['odd'] = np.extract(np.invert(in_central_patch), epsk)
for patch in ['even','odd']:
h = np.histogram(energies[patch], bins=n_bins, normed=True)
epsilon[patch] = 0.5 * (h[1][0:-1] + h[1][1:])
rho[patch] = h[0]
delta[patch] = h[1][1]-h[1][0]
# Construct the impurity solver
S = Solver(beta = beta,
gf_struct = {'even-up':[0], 'odd-up':[0], 'even-down':[0], 'odd-down':[0]}, n_l = 100)
# The local lattice Green's function
G = S.G0_iw.copy()
# Rotation
cn, cn_dag, nn = {}, {}, {}
for spin in ['up','down']:
cn['1-%s'%spin] = (c('even-%s'%spin,0) + c('odd-%s'%spin,0)) / np.sqrt(2)
cn['2-%s'%spin] = (c('even-%s'%spin,0) - c('odd-%s'%spin,0)) / np.sqrt(2)
nn['1-%s'%spin] = dagger(cn['1-%s'%spin]) * cn['1-%s'%spin]
nn['2-%s'%spin] = dagger(cn['2-%s'%spin]) * cn['2-%s'%spin]
# Local Hamiltonian
h_loc = U * (nn['1-up'] * nn['1-down'] + nn['2-up'] * nn['2-down'])
from pytriqs.gf import *
from pytriqs.operators import *
from pytriqs.applications.impurity_solvers.cthyb import Solver
# Parameters
D = 1.0 # Half-bandwidth of the bath
V = 0.2 # Hybridisation amplitude
U = 4.0 # Coulomb interaction
e_f = -U / 2 # Local energy level
h = 0.01 # External field
beta = 50 # Inverse temperature
# Construct the impurity solver with the inverse temperature
# and the structure of the Green's functions
S = Solver(beta=beta, gf_struct=[('up', [0]), ('down', [0])])
# Initialize the non-interacting Green's function S.G0_iw
# External magnetic field introduces Zeeman energy splitting between
# different spin components
S.G0_iw['up'] << inverse(iOmega_n - e_f + h / 2 - V**2 * Wilson(D))
S.G0_iw['down'] << inverse(iOmega_n - e_f - h / 2 - V**2 * Wilson(D))
# Run the solver
S.solve(
h_int=U * n('up', 0) * n('down', 0), # Local Hamiltonian
n_cycles=500000, # Number of QMC cycles
length_cycle=200, # Length of one cycle
n_warmup_cycles=10000, # Warmup cycles
measure_density_matrix=True, # Measure the reduced density matrix
use_norm_as_weight=True) # Required to measure the density matrix
from pytriqs.gf.local import *
from pytriqs.operators import *
from pytriqs.archive import *
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.cthyb import Solver
# Set up a few parameters
U = 2.5
half_bandwidth = 1.0
chemical_potential = U/2.0
beta = 100
n_loops = 5
# Construct the CTQMC solver
S = Solver(beta = beta, gf_struct = {'up':[0], 'down':[0]})
# Initalize the Green's function to a semi circular DOS
S.G_iw << SemiCircular(half_bandwidth)
# Now do the DMFT loop
for i_loop in range(n_loops):
# Compute new S.G0_iw with the self-consistency condition while imposing paramagnetism
g = 0.5 * (S.G_iw['up'] + S.G_iw['down'])
for name, g0 in S.G0_iw:
g0 << inverse(iOmega_n + chemical_potential - (half_bandwidth/2.0)**2 * g)
# Run the solver
S.solve(h_int = U * n('up',0) * n('down',0), # Local Hamiltonian
n_cycles = 5000, # Number of QMC cycles
length_cycle = 200, # Length of a cycle
def __init__(self, seedname, params):
"""
Initialize solver at each inequivalent correlation shell
Parameters
----------
seedname : string
seedname
params : dict
Input parameters
"""
self._params = copy.deepcopy(params)
# Construct a SumKDFT object
self.SK = SumkDFT(hdf_file=seedname + '.h5',
use_dft_blocks=False,
h_field=0.0)
u_file = HDFArchive(seedname + '.h5', 'r')
self.Umat = u_file["DCore"]["Umat"]
# Construct an impurity solver
beta = float(params['system']['beta'])
n_iw = int(params['system']['n_iw']) # Number of Matsubara frequencies
n_tau = int(params['system']['n_tau']) # Number of tau points
self.solver_name = params['impurity_solver']['name']
self._solver_params = create_solver_params(params['impurity_solver'])
if 'verbosity' in self._solver_params.keys():
if not mpi.is_master_node():
del self._solver_params['verbosity']
self.S = []
for ish in range(self.SK.n_inequiv_shells):
# Use GF structure determined by DFT blocks
gf_struct = self.SK.gf_struct_solver[ish]
if self.solver_name == "TRIQS/cthyb":
from pytriqs.applications.impurity_solvers.cthyb import Solver
if params['system']['n_l'] > 0:
self._solver_params['measure_g_l'] = True
self._solver_params['measure_g_tau'] = False
self.S.append(
Solver(beta=beta,
gf_struct=gf_struct,
n_iw=n_iw,
n_tau=n_tau,
n_l=params['system']['n_l']))
else:
self.S.append(
Solver(beta=beta,
gf_struct=gf_struct,
n_iw=n_iw,
n_tau=n_tau))
elif self.solver_name == "TRIQS/hubbard-I":
n_orb = self.SK.corr_shells[
self.SK.inequiv_to_corr[ish]]['dim'] / (self.SK.SO + 1)
from hubbard_solver_matrix import Solver
self.S.append(
Solver(beta=beta,
norb=n_orb,
n_msb=n_iw,
use_spin_orbit=self.SK.SO))
elif self.solver_name == "ALPS/cthyb":
from pytriqs.applications.impurity_solvers.alps_cthyb import Solver
if params['system']['n_l'] > 0:
self.S.append(
Solver(beta=beta,
gf_struct=gf_struct,
assume_real=False,
n_iw=n_iw,
n_tau=n_tau,
n_l=params['system']['n_l']))
else:
self.S.append(
Solver(beta=beta,
gf_struct=gf_struct,
assume_real=False,
n_iw=n_iw,
n_tau=n_tau))
else:
raise RuntimeError("Unknown solver " + self.solver_name)
