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[doc] Clean and merge python example scripts

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Manuel 2018-12-13 14:23:00 -05:00
parent 5a8996731d
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 triqs_cthyb import *
from pytriqs.gf import * from pytriqs.gf import *
from triqs_dft_tools.sumk_dft import * from triqs_dft_tools.sumk_dft import *
from triqs_dft_tools.converters.wien2k_converter import *
dft_filename='SrVO3' dft_filename='SrVO3'
U = 4.0
J = 0.65
beta = 40 beta = 40
loops = 15 # Number of DMFT sc-loops loops = 15 # Number of DMFT sc-loops
sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM 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 use_blocks = True # use bloc structure from DFT input
prec_mu = 0.0001 prec_mu = 0.0001
h_field = 0.0 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 # Solver parameters
p = {} p = {}
p["max_time"] = -1 p["max_time"] = -1
@ -46,7 +65,6 @@ if mpi.is_master_node():
previous_runs = ar['iterations'] previous_runs = ar['iterations']
else: else:
f.create_group('dmft_output') f.create_group('dmft_output')
previous_runs = mpi.bcast(previous_runs) previous_runs = mpi.bcast(previous_runs)
previous_present = mpi.bcast(previous_present) 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 # Use GF structure determined by DFT blocks
gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()] gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
# Construct U matrix for density-density calculations # Construct Solver
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")
S = Solver(beta=beta, gf_struct=gf_struct) S = Solver(beta=beta, gf_struct=gf_struct)
if previous_present: 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] S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
# Calculate new G0_iw to input into the solver: # Calculate new G0_iw to input into the solver:
if mpi.is_master_node(): S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
# 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: # Solve the impurity problem:
S.solve(h_int=h_int, **p) 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) 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.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 << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['G_iw']
S.G_iw << mpi.bcast(S.G_iw) S.G_iw << mpi.bcast(S.G_iw)
S.Sigma_iw << mpi.bcast(S.Sigma_iw) S.Sigma_iw << mpi.bcast(S.Sigma_iw)
# Write the final Sigma and G to the hdf5 archive: # Write the final Sigma and G to the hdf5 archive:
if mpi.is_master_node(): 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']['iterations'] = iteration_number + previous_runs
ar['dmft_output']['G_tau'] = S.G_tau ar['dmft_output']['G_tau'] = S.G_tau
ar['dmft_output']['G_iw'] = S.G_iw 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 more efficient. This is a more advanced example, which is
also suited for parallel execution. also suited for parallel execution.
For the convenience of the user, we provide also two For the convenience of the user, we provide also a full
working python scripts in this documentation. One for a calculation python script (:download:`dft_dmft_cthyb.py <images_scripts/dft_dmft_cthyb.py>`).
using Kanamori definitions (:download:`dft_dmft_cthyb.py The user has to adapt it to his own needs. How to execute your script is described :ref:`here<runpy>`.
<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>`.
The conversion will now be discussed in detail for the Wien2k and VASP packages. The conversion will now be discussed in detail for the Wien2k and VASP packages.
For more details we refer to the :ref:`documentation <conversion>`. For more details we refer to the :ref:`documentation <conversion>`.