from pytriqs.archive import *
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.ctint_tutorial import CtintSolver
from pytriqs.plot.mpl_interface import oplot
# Parameters
U = 2.5 # Hubbard interaction
mu = U / 2.0 # Chemical potential
half_bandwidth = 1.0 # Half bandwidth (energy unit)
beta = 40.0 # Inverse temperature
n_iw = 128 # Number of Matsubara frequencies
n_cycles = 10000 # Number of MC cycles
delta = 0.1 # delta parameter
n_iterations = 21 # Number of DMFT iterations
S = CtintSolver(beta, n_iw) # Initialize the solver
S.G_iw << SemiCircular(half_bandwidth) # Initialize the Green's function
with HDFArchive("dmft_bethe.output.h5", "w") as A:
A["n_iterations"] = n_iterations # Save a parameter
for it in range(n_iterations): # DMFT loop
for name, G0 in S.G0_iw:
G0 << inverse(iOmega_n + mu - (half_bandwidth / 2.0) ** 2 * S.G_iw[name]) # Set G0
# Change random number generator on final iteration
random_name = "mt19937" if it < n_iterations - 1 else "lagged_fibonacci19937"
S.solve(U, delta, n_cycles, random_name=random_name) # Solve the impurity problem
G_sym = (S.G_iw["up"] + S.G_iw["down"]) / 2 # Impose paramagnetic solution
from pytriqs.gf.local import *
from pytriqs.archive import *
from numpy import zeros
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.ctint_tutorial import CtintSolver
# Parameters
beta = 20.0
n_iw = 200;
U = 1.0
delta = 0.1;
n_cycles = 10000;
S = CtintSolver(beta, n_iw)
# init the Green function
mu = 1.3 - U/2
eps0 = 0.2
S.G0_iw << inverse(iOmega_n + mu - 1.0 * inverse(iOmega_n - eps0))
S.solve(U, delta, n_cycles)
if mpi.is_master_node():
A = HDFArchive("anderson.output.h5",'w')
A['G'] = S.G_iw['up']
from pytriqs.gf.local import *
from pytriqs.archive import *
from numpy import zeros
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.ctint_tutorial import CtintSolver
# Parameters
beta = 20.0
n_iw = 200
U = 1.0
delta = 0.1
n_cycles = 10000
S = CtintSolver(beta, n_iw)
# init the Green function
mu = 1.3 - U / 2
eps0 = 0.2
S.G0_iw << inverse(iOmega_n + mu - 1.0 * inverse(iOmega_n - eps0))
S.solve(U, delta, n_cycles)
if mpi.is_master_node():
A = HDFArchive("anderson.output.h5", 'w')
A['G'] = S.G_iw['up']
from pytriqs.archive import *
import pytriqs.utility.mpi as mpi
from pytriqs.applications.impurity_solvers.ctint_tutorial import CtintSolver
from pytriqs.plot.mpl_interface import oplot
# Parameters
U = 2.5 # Hubbard interaction
mu = U/2.0 # Chemical potential
half_bandwidth=1.0 # Half bandwidth (energy unit)
beta = 40.0 # Inverse temperature
n_iw = 128 # Number of Matsubara frequencies
n_cycles = 10000 # Number of MC cycles
delta = 0.1 # delta parameter
n_iterations = 21 # Number of DMFT iterations
S = CtintSolver(beta, n_iw) # Initialize the solver
S.G_iw << SemiCircular(half_bandwidth) # Initialize the Green's function
with HDFArchive("dmft_bethe.output.h5",'w') as A:
A['n_iterations'] = n_iterations # Save a parameter
for it in range(n_iterations): # DMFT loop
for name, G0 in S.G0_iw:
G0 << inverse(iOmega_n + mu - (half_bandwidth/2.0)**2 * S.G_iw[name] ) # Set G0
# Change random number generator on final iteration
random_name = 'mt19937' if it<n_iterations-1 else 'lagged_fibonacci19937'
S.solve(U, delta, n_cycles, random_name=random_name) # Solve the impurity problem
G_sym = (S.G_iw['up']+S.G_iw['down'])/2 # Impose paramagnetic solution
