previous_runs = 0
previous_present = False
del ar
MPI.barrier()
previous_runs = MPI.bcast(previous_runs)
previous_present = MPI.bcast(previous_present)
# Init the SumK class
SK=SumK_LDA(HDFfile=LDAFilename+'.h5',UseLDABlocs=False)
Norb = SK.corr_shells[0][3]
l = SK.corr_shells[0][2]
# Init the Solver:
S = Solver_HubbardI(Beta = Beta, Uint = Uint, JHund = JHund, l = l, Verbosity=2)
S.Nmoments=10
if (previous_present):
# load previous data:
MPI.report("Using stored data for initialisation")
if (MPI.IS_MASTER_NODE()):
ar = HDF_Archive(HDFfilename,'a')
S.Sigma <<= ar['SigmaF']
del ar
S.Sigma = MPI.bcast(S.Sigma)
SK.load()
# DMFT loop:
for Iteration_Number in range(1,Loops+1):
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# TRIQS. If not, see <http://www.gnu.org/licenses/>.
#
################################################################################
from pytriqs.Base.Archive import *
from pytriqs.Solvers.HubbardI.Solver_HubbardI import Solver_HubbardI
import numpy
S = Solver_HubbardI(Beta=200, Uint=6.0, JHund=0.6, l=2, UseSpinOrbit=False)
eal = {}
eal['up'] = -1 * numpy.identity(5)
eal['down'] = -1 * numpy.identity(5)
S.set_atomic_levels(eal=eal)
S.Solve()
ar = HDF_Archive('HubbardI.output.h5')
ar['G'] = S.G
ar['Sigma'] = S.Sigma
del ar
MPI.barrier()
previous_runs = MPI.bcast(previous_runs)
previous_present = MPI.bcast(previous_present)
# if previous runs are present, no need for recalculating the bloc structure
# It has to be commented, if you run this script for the first time, starting
# from a converted h5 archive.
# Init the SumK class
SK=SumK_LDA_tools(HDFfile=LDAFilename+'.h5',UseLDABlocs=False)
if (MPI.IS_MASTER_NODE()):
print 'DC after reading SK: ',SK.dc_imp[SK.invshellmap[0]]
N = SK.corr_shells[0][3]
l = SK.corr_shells[0][2]
# Init the Solver:
S = Solver_HubbardI(Beta = Beta, Uint = Uint, JHund = JHund, l = l)
S.Nmoments=10
# set atomic levels:
eal = SK.eff_atomic_levels()[0]
S.set_atomic_levels( eal = eal )
S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om)
S.Sigma.save('S.Sigma')
SK.put_Sigma(Sigmaimp = [S.Sigma])
SK.DOSpartial(broadening=broadening)
MPI.barrier()
previous_runs = MPI.bcast(previous_runs)
previous_present = MPI.bcast(previous_present)
# if previous runs are present, no need for recalculating the bloc structure
# It has to be commented, if you run this script for the first time, starting
# from a converted h5 archive.
# Init the SumK class
SK=SumK_LDA_tools(HDFfile=LDAFilename+'.h5',UseLDABlocs=False)
if (MPI.IS_MASTER_NODE()):
print 'DC after reading SK: ',SK.dc_imp[SK.invshellmap[0]]
N = SK.corr_shells[0][3]
l = SK.corr_shells[0][2]
# Init the Solver:
S = Solver_HubbardI(Beta = Beta, Uint = Uint, JHund = JHund, l = l)
S.Nmoments=10
# set atomic levels:
eal = SK.eff_atomic_levels()[0]
S.set_atomic_levels( eal = eal )
S.GF_realomega(ommin=ommin, ommax = ommax, N_om=N_om)
S.Sigma.save('S.Sigma')
SK.put_Sigma(Sigmaimp = [S.Sigma])
SK.DOSpartial(broadening=broadening)
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# TRIQS. If not, see <http://www.gnu.org/licenses/>.
#
################################################################################
from pytriqs.Base.Archive import *
from pytriqs.Solvers.HubbardI.Solver_HubbardI import Solver_HubbardI
import numpy
S = Solver_HubbardI(Beta = 200, Uint = 6.0, JHund=0.6, l=2, UseSpinOrbit=False)
eal={}
eal['up'] = -1*numpy.identity(5)
eal['down'] = -1*numpy.identity(5)
S.set_atomic_levels(eal=eal)
S.Solve()
ar=HDF_Archive('HubbardI.output.h5')
ar['G'] = S.G
ar['Sigma'] = S.Sigma
del ar
previous_runs = 0
previous_present = False
del ar
MPI.barrier()
previous_runs = MPI.bcast(previous_runs)
previous_present = MPI.bcast(previous_present)
# Init the SumK class
SK = SumK_LDA(HDFfile=LDAFilename + '.h5', UseLDABlocs=False)
Norb = SK.corr_shells[0][3]
l = SK.corr_shells[0][2]
# Init the Solver:
S = Solver_HubbardI(Beta=Beta, Uint=Uint, JHund=JHund, l=l, Verbosity=2)
S.Nmoments = 10
if (previous_present):
# load previous data:
MPI.report("Using stored data for initialisation")
if (MPI.IS_MASTER_NODE()):
ar = HDF_Archive(HDFfilename, 'a')
S.Sigma <<= ar['SigmaF']
del ar
S.Sigma = MPI.bcast(S.Sigma)
SK.load()
# DMFT loop:
for Iteration_Number in range(1, Loops + 1):
