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[doc] Clean and merge python example scripts

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Manuel 2018-12-13 14:23:00 -05:00
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commit 4bbbcf93f1
3 changed files with 30 additions and 181 deletions
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52
doc/tutorials/images_scripts/dft_dmft_cthyb.py
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@ -4,19 +4,38 @@ from pytriqs.archive import HDFArchive
from triqs_cthyb import *
from pytriqs.gf import *
from triqs_dft_tools.sumk_dft import *
from triqs_dft_tools.converters.wien2k_converter import *
dft_filename='SrVO3'
U = 4.0
J = 0.65
beta = 40
loops = 15 # Number of DMFT sc-loops
sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
use_blocks = True # use bloc structure from DFT input
prec_mu = 0.0001
h_field = 0.0
## KANAMORI DENSITY-DENSITY (for full Kanamori use h_int_kanamori)
# Define interaction paramters, DC and Hamiltonian
U = 4.0
J = 0.65
dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
# Construct U matrix for density-density calculations
Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
# Construct density-density Hamiltonian
h_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
## SLATER HAMILTONIAN
## Define interaction paramters, DC and Hamiltonian
#U = 9.6
#J = 0.8
#dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
## Construct Slater U matrix
#U_sph = U_matrix(l=2, U_int=U, J_hund=J)
#U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention='wien2k'))
#Umat = t2g_submatrix(U_cubic, convention='wien2k')
## Construct Slater Hamiltonian
#h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
# Solver parameters
p = {}
p["max_time"] = -1
@ -46,7 +65,6 @@ if mpi.is_master_node():
previous_runs = ar['iterations']
else:
f.create_group('dmft_output')
previous_runs = mpi.bcast(previous_runs)
previous_present = mpi.bcast(previous_present)
@ -60,11 +78,7 @@ orb_names = [i for i in range(n_orb)]
# Use GF structure determined by DFT blocks
gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
# Construct U matrix for density-density calculations
Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
# Construct density-density Hamiltonian and solver
h_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat, H_dump="H.txt")
# Construct Solver
S = Solver(beta=beta, gf_struct=gf_struct)
if previous_present:
@ -98,19 +112,7 @@ for iteration_number in range(1,loops+1):
S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
# Calculate new G0_iw to input into the solver:
if mpi.is_master_node():
# We can do a mixing of Delta in order to stabilize the DMFT iterations:
S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
# The following lines are uncommented until issue #98 is fixed in TRIQS
# with HDFArchive(dft_filename+'.h5','a') as ar:
# if (iteration_number>1 or previous_present):
# mpi.report("Mixing input Delta with factor %s"%delta_mix)
# Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['dmft_output']['Delta_iw']
# S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
# ar['dmft_output']['Delta_iw'] = delta(S.G0_iw)
S.G0_iw << inverse(S.G0_iw)
S.G0_iw << mpi.bcast(S.G0_iw)
S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
# Solve the impurity problem:
S.solve(h_int=h_int, **p)
@ -125,13 +127,13 @@ for iteration_number in range(1,loops+1):
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['dmft_output']['Sigma_iw']
S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['G_iw']
S.G_iw << mpi.bcast(S.G_iw)
S.Sigma_iw << mpi.bcast(S.Sigma_iw)
# Write the final Sigma and G to the hdf5 archive:
if mpi.is_master_node():
with ar = HDFArchive(dft_filename+'.h5','a') as ar:
with HDFArchive(dft_filename+'.h5','a') as ar:
ar['dmft_output']['iterations'] = iteration_number + previous_runs
ar['dmft_output']['G_tau'] = S.G_tau
ar['dmft_output']['G_iw'] = S.G_iw

149
doc/tutorials/images_scripts/dft_dmft_cthyb_slater.py
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@ -1,149 +0,0 @@
import pytriqs.utility.mpi as mpi
from pytriqs.operators.util import *
from pytriqs.archive import HDFArchive
from triqs_cthyb import *
from pytriqs.gf import *
from triqs_dft_tools.sumk_dft import *
from triqs_dft_tools.converters.wien2k_converter import *
dft_filename='SrVO3'
U = 9.6
J = 0.8
beta = 40
loops = 10 # Number of DMFT sc-loops
sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
use_blocks = True # use bloc structure from DFT input
prec_mu = 0.0001
h_field = 0.0
# Solver parameters
p = {}
p["max_time"] = -1
p["random_seed"] = 123 * mpi.rank + 567
p["length_cycle"] = 200
p["n_warmup_cycles"] = 100000
p["n_cycles"] = 1000000
p["perform_tail_fit"] = True
p["fit_max_moment"] = 4
p["fit_min_n"] = 30
p["fit_max_n"] = 60
# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
#from triqs_dft_tools.converters.wien2k_converter import *
#Converter = Wien2kConverter(filename=dft_filename, repacking=True)
#Converter.convert_dft_input()
#mpi.barrier()
previous_runs = 0
previous_present = False
if mpi.is_master_node():
with HDFArchive(dft_filename+'.h5','a') as f:
if 'dmft_output' in f:
ar = f['dmft_output']
if 'iterations' in ar:
previous_present = True
previous_runs = ar['iterations']
else:
f.create_group('dmft_output')
previous_runs = mpi.bcast(previous_runs)
previous_present = mpi.bcast(previous_present)
SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks,h_field=h_field)
n_orb = SK.corr_shells[0]['dim']
l = SK.corr_shells[0]['l']
spin_names = ["up","down"]
orb_names = [i for i in range(n_orb)]
# Use GF structure determined by DFT blocks
gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
# Construct Slater U matrix
U_sph = U_matrix(l=2, U_int=U, J_hund=J)
U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention='wien2k'))
Umat = t2g_submatrix(U_cubic, convention='wien2k')
# Construct Hamiltonian and solver
h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
S = Solver(beta=beta, gf_struct=gf_struct)
if previous_present:
chemical_potential = 0
dc_imp = 0
dc_energ = 0
if mpi.is_master_node():
with HDFArchive(dft_filename+'.h5','r') as ar:
S.Sigma_iw << ar['dmft_output']['Sigma_iw']
chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
S.Sigma_iw << mpi.bcast(S.Sigma_iw)
chemical_potential = mpi.bcast(chemical_potential)
dc_imp = mpi.bcast(dc_imp)
dc_energ = mpi.bcast(dc_energ)
SK.set_mu(chemical_potential)
SK.set_dc(dc_imp,dc_energ)
for iteration_number in range(1,loops+1):
if mpi.is_master_node(): print "Iteration = ", iteration_number
SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
SK.set_Sigma([ S.Sigma_iw ]) # set Sigma into the SumK class
chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for given density
S.G_iw << SK.extract_G_loc()[0] # calc the local Green function
mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
# Init the DC term and the real part of Sigma, if no previous runs found:
if (iteration_number==1 and previous_present==False):
dm = S.G_iw.density()
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
# Calculate new G0_iw to input into the solver:
if mpi.is_master_node():
# We can do a mixing of Delta in order to stabilize the DMFT iterations:
S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
# The following lines are uncommented until issue #98 is fixed in TRIQS
# with HDFArchive(dft_filename+'.h5','a') as ar:
# if (iteration_number>1 or previous_present):
# mpi.report("Mixing input Delta with factor %s"%delta_mix)
# Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['dmft_output']['Delta_iw']
# S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
# ar['dmft_output']['Delta_iw'] = delta(S.G0_iw)
S.G0_iw << inverse(S.G0_iw)
S.G0_iw << mpi.bcast(S.G0_iw)
# Solve the impurity problem:
S.solve(h_int=h_int, **p)
# Solved. Now do post-processing:
mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density())
# Now mix Sigma and G with factor sigma_mix, if wanted:
if (iteration_number>1 or previous_present):
if mpi.is_master_node():
with HDFArchive(dft_filename+'.h5','r') as ar:
mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['dmft_output']['Sigma_iw']
S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['G_iw']
S.G_iw << mpi.bcast(S.G_iw)
S.Sigma_iw << mpi.bcast(S.Sigma_iw)
# Write the final Sigma and G to the hdf5 archive:
if mpi.is_master_node():
with HDFArchive(dft_filename+'.h5','a') as ar:
ar['dmft_output']['iterations'] = iteration_number + previous_runs
ar['dmft_output']['G_tau'] = S.G_tau
ar['dmft_output']['G_iw'] = S.G_iw
ar['dmft_output']['Sigma_iw'] = S.Sigma_iw
ar['dmft_output']['G0-%s'%(iteration_number)] = S.G0_iw
ar['dmft_output']['G-%s'%(iteration_number)] = S.G_iw
ar['dmft_output']['Sigma-%s'%(iteration_number)] = S.Sigma_iw
# Set the new double counting:
dm = S.G_iw.density() # compute the density matrix of the impurity problem
SK.calc_dc(dm, U_interact = U, J_hund = J, orb = 0, use_dc_formula = dc_type)
# Save stuff into the dft_output group of hdf5 archive in case of rerun:
SK.save(['chemical_potential','dc_imp','dc_energ'])

10
doc/tutorials/srvo3.rst
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@ -6,13 +6,9 @@ Some additional parameter are introduced to make the calculation
more efficient. This is a more advanced example, which is
also suited for parallel execution.
For the convenience of the user, we provide also two
working python scripts in this documentation. One for a calculation
using Kanamori definitions (:download:`dft_dmft_cthyb.py
<images_scripts/dft_dmft_cthyb.py>`) and one with a
rotational-invariant Slater interaction Hamiltonian (:download:`dft_dmft_cthyb_slater.py
<images_scripts/dft_dmft_cthyb_slater.py>`). The user has to adapt these
scripts to his own needs. How to execute your script is described :ref:`here<runpy>`.
For the convenience of the user, we provide also a full
python script (:download:`dft_dmft_cthyb.py <images_scripts/dft_dmft_cthyb.py>`).
The user has to adapt it to his own needs. How to execute your script is described :ref:`here<runpy>`.
The conversion will now be discussed in detail for the Wien2k and VASP packages.
For more details we refer to the :ref:`documentation <conversion>`.