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[doc] Corrected errors in the Ce example scripts

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This commit is contained in:
Manuel Zingl 2016-01-18 11:04:55 +01:00
parent 8652660115
commit 919e0a3ead
3 changed files with 23 additions and 28 deletions
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2
doc/guide/dftdmft_singleshot.rst
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@ -145,7 +145,7 @@ Most of these parameters are self-explanatory. The first,
details on the solver parameters, we refer the user to details on the solver parameters, we refer the user to
the :ref:`CTHYB solver <triqscthyb:welcome>` documentation. the :ref:`CTHYB solver <triqscthyb:welcome>` documentation.
We assume that the conversion to the hdf5 archive is alreadz done. We We assume that the conversion to the hdf5 archive is already done. We
can check now in this archive, if previous runs are present, or if we have to start can check now in this archive, if previous runs are present, or if we have to start
from scratch:: from scratch::

35
doc/guide/images_scripts/Ce-gamma.py
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@ -2,7 +2,6 @@ from pytriqs.applications.dft.sumk_dft import *
from pytriqs.applications.dft.converters.wien2k_converter import * from pytriqs.applications.dft.converters.wien2k_converter import *
from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver from pytriqs.applications.impurity_solvers.hubbard_I.hubbard_solver import Solver
import os import os
dft_filename = os.getcwd().rpartition('/')[2] dft_filename = os.getcwd().rpartition('/')[2]
@ -10,14 +9,12 @@ beta = 40
U_int = 6.00 U_int = 6.00
J_hund = 0.70 J_hund = 0.70
Loops = 5 # Number of DMFT sc-loops Loops = 5 # Number of DMFT sc-loops
Mix = 0.7 # Mixing factor in QMC mixing = 0.7 # Mixing factor
DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein DC_type = 0 # 0...FLL, 1...Held, 2... AMF, 3...Lichtenstein
chemical_potential_init=0.0 # initial chemical potential chemical_potential_init=0.0 # initial chemical potential
HDFfilename = dft_filename+'.h5'
# Convert DMFT input: # Convert DMFT input:
Converter = Wien2kConverter(filename=filename) Converter = Wien2kConverter(filename=dft_filename)
Converter.convert_dft_input() Converter.convert_dft_input()
mpi.barrier() mpi.barrier()
@ -25,7 +22,7 @@ mpi.barrier()
previous_runs = 0 previous_runs = 0
previous_present = False previous_present = False
if mpi.is_master_node(): if mpi.is_master_node():
f = HDFArchive(filename+'.h5','a') f = HDFArchive(dft_filename+'.h5','a')
if 'dmft_output' in f: if 'dmft_output' in f:
ar = f['dmft_output'] ar = f['dmft_output']
if 'iterations' in ar: if 'iterations' in ar:
@ -53,11 +50,11 @@ if previous_present:
ar = HDFArchive(dft_filename+'.h5','a') ar = HDFArchive(dft_filename+'.h5','a')
S.Sigma << ar['dmft_output']['Sigma'] S.Sigma << ar['dmft_output']['Sigma']
del ar del ar
chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ']) SK.chemical_potential,SK.dc_imp,SK.dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
S.Sigma << mpi.bcast(S.Sigma) S.Sigma << mpi.bcast(S.Sigma)
SK.set_mu(chemical_potential) SK.chemical_potential = mpi.bcast(SK.chemical_potential)
SK.set_dc(dc_imp,dc_energ) SK.dc_imp = mpi.bcast(SK.dc_imp)
SK.dc_energ = mpi.bcast(SK.dc_energ)
# DMFT loop: # DMFT loop:
for iteration_number in range(1,Loops+1): for iteration_number in range(1,Loops+1):
@ -78,7 +75,7 @@ for iteration_number in range(1,Loops+1):
if ((iteration_number==1)and(previous_present==False)): if ((iteration_number==1)and(previous_present==False)):
dc_value_init=U_int/2.0 dc_value_init=U_int/2.0
dm=S.G.density() dm=S.G.density()
SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init) SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type, use_dc_value=dc_value_init)
# calculate non-interacting atomic level positions: # calculate non-interacting atomic level positions:
eal = SK.eff_atomic_levels()[0] eal = SK.eff_atomic_levels()[0]
@ -89,11 +86,11 @@ for iteration_number in range(1,Loops+1):
# Now mix Sigma and G with factor Mix, if wanted: # Now mix Sigma and G with factor Mix, if wanted:
if (iteration_number>1 or previous_present): if (iteration_number>1 or previous_present):
if (mpi.is_master_node() and (sigma_mix<1.0)): if (mpi.is_master_node() and (mixing<1.0)):
ar = HDFArchive(dft_filename+'.h5','a') ar = HDFArchive(dft_filename+'.h5','a')
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix) mpi.report("Mixing Sigma and G with factor %s"%mixing)
S.Sigma << sigma_mix * S.Sigma + (1.0-sigma_mix) * ar['dmft_output']['Sigma'] S.Sigma << mixing * S.Sigma + (1.0-mixing) * ar['dmft_output']['Sigma']
S.G << sigma_mix * S.G + (1.0-sigma_mix) * ar['dmft_output']['G'] S.G << mixing * S.G + (1.0-mixing) * ar['dmft_output']['G']
del ar del ar
S.G << mpi.bcast(S.G) S.G << mpi.bcast(S.G)
S.Sigma << mpi.bcast(S.Sigma) S.Sigma << mpi.bcast(S.Sigma)
@ -104,8 +101,8 @@ for iteration_number in range(1,Loops+1):
SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type ) SK.calc_dc( dm, U_interact = U_int, J_hund = J_hund, orb = 0, use_dc_formula = DC_type )
# correlation energy calculations: # correlation energy calculations:
correnerg = 0.5 * (S.G * S.Sigma).total_density() SK.correnerg = 0.5 * (S.G * S.Sigma).total_density()
mpi.report("Corr. energy = %s"%correnerg) mpi.report("Corr. energy = %s"%SK.correnerg)
# store the impurity self-energy, GF as well as correlation energy in h5 # store the impurity self-energy, GF as well as correlation energy in h5
if mpi.is_master_node(): if mpi.is_master_node():
@ -145,7 +142,7 @@ mpi.report("Trace of Density Matrix: %s"%d)
# store correlation energy contribution to be read by Wien2ki and then included to DFT+DMFT total energy # store correlation energy contribution to be read by Wien2ki and then included to DFT+DMFT total energy
if (mpi.is_master_node()): if (mpi.is_master_node()):
correnerg -= DCenerg[0] SK.correnerg -= SK.dc_energ[0]
f=open(dft_filename+'.qdmft','a') f=open(dft_filename+'.qdmft','a')
f.write("%.16f\n"%correnerg) f.write("%.16f\n"%SK.correnerg)
f.close() f.close()

12
doc/guide/images_scripts/Ce-gamma_DOS.py
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@ -14,23 +14,21 @@ ommax=6.0
N_om=2001 N_om=2001
broadening = 0.02 broadening = 0.02
HDFfilename = dft_filename+'.h5'
# Convert DMFT input: # Convert DMFT input:
Converter = Wien2kConverter(filename=dft_filename,repacking=True) Converter = Wien2kConverter(filename=dft_filename,repacking=True)
Converter.convert_dft_input() Converter.convert_dft_input()
Converter.convert_parproj_input() Converter.convert_parproj_input()
# Init the SumK class # Init the SumK class
SK = SumkDFTTools(hdf_file=dft_filename+'.h5',use_dft_blocks=False) SK = SumkDFTTools(hdf_file=dft_filename+'.h5',use_dft_blocks=False)
# load old chemical potential and DC # load old chemical potential and DC
if mpi.is_master_node(): if mpi.is_master_node():
chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ']) SK.chemical_potential,SK.dc_imp,SK.dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
SK.set_mu(chemical_potential)
SK.set_dc(dc_imp,dc_energ) SK.chemical_potential = mpi.bcast(SK.chemical_potential)
SK.dc_imp = mpi.bcast(SK.dc_imp)
SK.dc_energ = mpi.bcast(SK.dc_energ)
if (mpi.is_master_node()): if (mpi.is_master_node()):
print 'DC after reading SK: ',SK.dc_imp[0] print 'DC after reading SK: ',SK.dc_imp[0]