# 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.hubbard_I.solver import Solver
import numpy
S = Solver(beta=200, U_int=6.0, J_hund=0.6, l=2, use_spin_orbit=False)
eal = {}
eal["up"] = -1 * numpy.identity(5)
eal["down"] = -1 * numpy.identity(5)
S.set_atomic_levels(eal=eal)
S.Solve()
ar = HDFArchive("hubbard.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=SumkLDA(hdf_file=LDAFilename+'.h5',use_lda_blocks=False)
Norb = SK.corr_shells[0][3]
l = SK.corr_shells[0][2]
# Init the Solver:
S = Solver(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 = HDFArchive(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):
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 = SumkLDATools(hdf_file=LDAFilename+'.h5',use_lda_blocks=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(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.dos_partial(broadening=broadening)
