def build_flow(options):
flow = make_base_flow(options)
optic_input = abilab.OpticInput(
broadening=0.002,
domega=0.0003,
maxomega=0.3,
scissor=0.000,
tolerance=0.002,
num_lin_comp=1,
lin_comp=11,
num_nonlin_comp=2,
nonlin_comp=(123, 222),
)
mpi_list = options.mpi_list
if mpi_list is None:
mpi_list = [1, 2, 4, 8]
print("Using mpi_list:", mpi_list)
else:
print("Using mpi_list from cmd line:", mpi_list)
work = flowtk.Work()
for mpi_procs, omp_threads in product(mpi_list, options.omp_list):
if not options.accept_mpi_omp(mpi_procs, omp_threads): continue
manager = options.manager.new_with_fixed_mpi_omp(
mpi_procs, omp_threads)
optic_task = flowtk.OpticTask(optic_input,
manager=manager,
nscf_node=flow[0].nscf_task,
ddk_nodes=flow[1])
work.register_task(optic_task)
flow.register_work(work)
return flow.allocate()
def build_flow(options, paral_kgb=0):
# Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
workdir = options.workdir
if not options.workdir:
workdir = os.path.basename(__file__).replace(".py", "").replace(
"run_", "flow_")
multi = abilab.MultiDataset(structure=data.structure_from_ucell("GaAs"),
pseudos=data.pseudos("31ga.pspnc",
"33as.pspnc"),
ndtset=5)
# Global variables
kmesh = dict(ngkpt=[4, 4, 4],
nshiftk=4,
shiftk=[[0.5, 0.5, 0.5], [0.5, 0.0, 0.0], [0.0, 0.5, 0.0],
[0.0, 0.0, 0.5]])
global_vars = dict(ecut=2, paral_kgb=paral_kgb)
global_vars.update(kmesh)
multi.set_vars(global_vars)
# Dataset 1 (GS run)
multi[0].set_vars(
tolvrs=1e-6,
nband=4,
)
# NSCF run with large number of bands, and points in the the full BZ
multi[1].set_vars(
iscf=-2,
nband=20,
nstep=25,
kptopt=1,
tolwfr=1.e-9,
#kptopt=3,
)
# Fourth dataset : ddk response function along axis 1
# Fifth dataset : ddk response function along axis 2
# Sixth dataset : ddk response function along axis 3
for dir in range(3):
rfdir = 3 * [0]
rfdir[dir] = 1
multi[2 + dir].set_vars(
iscf=-3,
nband=20,
nstep=1,
nline=0,
prtwf=3,
kptopt=3,
nqpt=1,
qpt=[0.0, 0.0, 0.0],
rfdir=rfdir,
rfelfd=2,
tolwfr=1.e-9,
)
scf_inp, nscf_inp, ddk1, ddk2, ddk3 = multi.split_datasets()
# Initialize the flow.
flow = abilab.Flow(workdir, manager=options.manager, remove=options.remove)
bands_work = abilab.BandStructureWork(scf_inp, nscf_inp)
flow.register_work(bands_work)
ddk_work = abilab.Work()
for inp in [ddk1, ddk2, ddk3]:
ddk_work.register_ddk_task(inp, deps={bands_work.nscf_task: "WFK"})
flow.register_work(ddk_work)
# Optic does not support MPI with ncpus > 1.
optic_input = abilab.OpticInput(
broadening=0.002, # Value of the smearing factor, in Hartree
domega=0.0003, # Frequency mesh.
maxomega=0.3,
scissor=0.000, # Scissor shift if needed, in Hartree
tolerance=0.002, # Tolerance on closeness of singularities (in Hartree)
num_lin_comp=
1, # Number of components of linear optic tensor to be computed
lin_comp=11, # Linear coefficients to be computed (x=1, y=2, z=3)
num_nonlin_comp=
2, # Number of components of nonlinear optic tensor to be computed
nonlin_comp=(123, 222), # Non-linear coefficients to be computed
)
# TODO
# Check is the order of the 1WF files is relevant. Can we use DDK files ordered
# in an arbitrary way or do we have to pass (x,y,z)?
optic_task = abilab.OpticTask(optic_input,
nscf_node=bands_work.nscf_task,
ddk_nodes=ddk_work)
flow.register_task(optic_task)
return flow
def raman_work(structure, pseudos, ngkpt, shiftk):
# Generate 3 different input files for computing optical properties with Optic.
global_vars = dict(
istwfk="*1",
paral_kgb=0,
ecut=8,
nstep=200,
diemac=12,
ixc=7,
chksymbreak=0,
#accesswff=3
)
multi = abilab.MultiDataset(structure, pseudos=pseudos, ndtset=5)
multi.set_vars(global_vars)
multi.set_kmesh(ngkpt=ngkpt, shiftk=shiftk)
# GS run
multi[0].set_vars(
tolvrs=1e-8,
nband=20,
nbdbuf=2,
)
# Note kptopt 2 in NSCF and DDK
# In principle kptopt 2 is needed only in DDK.
# one could do a first NSCF run with kptopt 1, reread with kptopt 2 and enter DDK...
# NSCF run
multi[1].set_vars(
iscf=-2,
nband=40,
nbdbuf=5,
kptopt=2,
tolwfr=1.e-12,
)
# DDK along 3 directions
# Third dataset : ddk response function along axis 1
# Fourth dataset : ddk response function along axis 2
# Fifth dataset : ddk response function along axis 3
for idir in range(3):
rfdir = 3 * [0]
rfdir[idir] = 1
multi[2 + idir].set_vars(
iscf=-3,
nband=40,
nbdbuf=5,
nstep=1,
nline=0,
prtwf=3,
kptopt=2,
nqpt=1,
qpt=[0.0, 0.0, 0.0],
rfdir=rfdir,
rfelfd=2,
tolwfr=1.e-12,
)
scf_inp, nscf_inp, ddk1, ddk2, ddk3 = multi.split_datasets()
ddk_inputs = [ddk1, ddk2, ddk3]
work = flowtk.Work()
scf_t = work.register_scf_task(scf_inp)
nscf_t = work.register_nscf_task(nscf_inp, deps={scf_t: "DEN"})
ddk_nodes = []
for inp in ddk_inputs:
ddk_t = work.register_ddk_task(inp, deps={nscf_t: "WFK"})
ddk_nodes.append(ddk_t)
optic_input = abilab.OpticInput(
broadening=0.002, # Value of the smearing factor, in Hartree
domega=0.0003, # Frequency mesh.
maxomega=0.3,
scissor=0.000, # Scissor shift if needed, in Hartree
tolerance=0.002, # Tolerance on closeness of singularities (in Hartree)
num_lin_comp=
6, # Number of components of linear optic tensor to be computed
lin_comp=(11, 12, 13, 22, 23,
33), # Linear coefficients to be computed (x=1, y=2, z=3)
num_nonlin_comp=
0 # Number of components of nonlinear optic tensor to be computed
#nonlin_comp=(123, 222),
)
optic_t = flowtk.OpticTask(optic_input,
nscf_node=nscf_t,
ddk_nodes=ddk_nodes)
work.register(optic_t)
return work
def build_flow(options, paral_kgb=0):
"""
Build flow for the calculation of optical properties with optic + band structure
along high-symmetry k-path. DDK are computed with 3 k-meshes of increasing density
to monitor the convergece of the spectra.
"""
# Working directory (default is the name of the script with '.py' removed and "run_" replaced by "flow_")
if not options.workdir:
options.workdir = os.path.basename(sys.argv[0]).replace(
".py", "").replace("run_", "flow_")
multi = abilab.MultiDataset(structure=abidata.structure_from_ucell("GaAs"),
pseudos=abidata.pseudos(
"31ga.pspnc", "33as.pspnc"),
ndtset=2)
# Usa same shifts in all tasks.
shiftk = [[0.5, 0.5, 0.5], [0.5, 0.0, 0.0], [0.0, 0.5, 0.0],
[0.0, 0.0, 0.5]]
# Global variables.
multi.set_vars(ecut=2, paral_kgb=paral_kgb)
# Dataset 1 (GS run)
multi[0].set_vars(tolvrs=1e-8, nband=4)
multi[0].set_kmesh(ngkpt=[4, 4, 4], shiftk=shiftk)
# NSCF run on k-path with large number of bands
multi[1].set_vars(iscf=-2, nband=20, tolwfr=1.e-9)
multi[1].set_kpath(ndivsm=10)
# Initialize the flow.
flow = flowtk.Flow(options.workdir, manager=options.manager)
# GS to get the density + NSCF along the path.
scf_inp, nscf_inp = multi.split_datasets()
bands_work = flowtk.BandStructureWork(scf_inp, nscf_inp)
flow.register_work(bands_work)
# Build OpticInput used to compute optical properties.
optic_input = abilab.OpticInput(
broadening=0.002, # Value of the smearing factor, in Hartree
domega=0.0003, # Frequency mesh.
maxomega=0.3,
scissor=0.000, # Scissor shift if needed, in Hartree
tolerance=0.002, # Tolerance on closeness of singularities (in Hartree)
num_lin_comp=
2, # Number of components of linear optic tensor to be computed
lin_comp=(11,
33), # Linear coefficients to be computed (x=1, y=2, z=3)
num_nonlin_comp=
2, # Number of components of nonlinear optic tensor to be computed
nonlin_comp=(123, 222), # Non-linear coefficients to be computed
)
# ddk_nband is fixed here, in principle it depends on nelect and the frequency range in chi(w).
ddk_nband = 20
# Perform converge study wrt ngkpt (shiftk is constant).
ngkpt_convergence = [[4, 4, 4], [8, 8, 8], [16, 16, 16]]
from abipy.flowtk.dfpt_works import NscfDdksWork
for ddk_ngkpt in ngkpt_convergence:
# Build work for NSCF from DEN produced by the first GS task + 3 DDKs.
# All tasks use more bands and a denser k-mesh defined by ddk_ngkpt.
ddks_work = NscfDdksWork.from_scf_task(bands_work[0], ddk_ngkpt,
shiftk, ddk_nband)
flow.register_work(ddks_work)
# Build optic task to compute chi with this value of ddk_ngkpt.
optic_task = flowtk.OpticTask(
optic_input,
nscf_node=ddks_work.task_with_ks_energies,
ddk_nodes=ddks_work.ddk_tasks,
use_ddknc=False)
ddks_work.register_task(optic_task)
return flow
"""
0.002 ! Value of the smearing factor, in Hartree
0.0003 0.3 ! Difference between frequency values (in Hartree), and maximum frequency ( 1 Ha is about 27.211 eV)
0.000 ! Scissor shift if needed, in Hartree
0.002 ! Tolerance on closeness of singularities (in Hartree)
6 ! Number of components of linear optic tensor to be computed
11 12 13 22 23 33 ! Linear coefficients to be computed (x=1, y=2, z=3)
0 ! Number of components of nonlinear optic tensor to be computed
! Non-linear coefficients to be computed
"""
optic_input = abilab.OpticInput(zcut=0.002,
wmesh=(0.0003, 0.3),
scissor=0.000,
sing_tol=0.002,
num_lin_comp=6,
lin_comp=(11, 12, 13, 22, 23, 33),
num_nonlin_comp=0
#nonlin_comp=(123, 222),
)
print(optic_input)
global_vars = dict(
istwfk="*1",
paral_kgb=0,
ecut=15,
nstep=500,
spinat=[[0.0000000000E+00, 0.0000000000E+00, 3.5716762600E+00],
[0.0000000000E+00, 0.0000000000E+00, -3.5716762600E+00],
[0.0000000000E+00, 0.0000000000E+00, 0.0000000000E+00],
def itest_optic_flow(fwp, tvars):
"""Test optic calculations."""
if tvars.paral_kgb == 1:
pytest.xfail("Optic flow with paral_kgb==1 is expected to fail (implementation problem)")
"""
0.002 ! Value of the smearing factor, in Hartree
0.0003 0.3 ! Difference between frequency values (in Hartree), and maximum frequency ( 1 Ha is about 27.211 eV)
0.000 ! Scissor shift if needed, in Hartree
0.002 ! Tolerance on closeness of singularities (in Hartree)
1 ! Number of components of linear optic tensor to be computed
11 ! Linear coefficients to be computed (x=1, y=2, z=3)
2 ! Number of components of nonlinear optic tensor to be computed
123 222 ! Non-linear coefficients to be computed
"""
optic_input = abilab.OpticInput(
broadening=0.002,
domega=0.0003,
maxomega=0.3,
scissor=0.000,
tolerance=0.002,
num_lin_comp=1,
lin_comp=11,
num_nonlin_comp=2,
nonlin_comp=(123, 222),
)
print(optic_input)
#raise ValueError()
scf_inp, nscf_inp, ddk1, ddk2, ddk3 = make_inputs(tvars)
flow = flowtk.Flow(fwp.workdir, manager=fwp.manager)
bands_work = flowtk.BandStructureWork(scf_inp, nscf_inp)
flow.register_work(bands_work)
# work with DDK tasks.
ddk_work = flowtk.Work()
for inp in [ddk1, ddk2, ddk3]:
ddk_work.register_ddk_task(inp, deps={bands_work.nscf_task: "WFK"})
flow.register_work(ddk_work)
flow.allocate()
flow.build_and_pickle_dump(abivalidate=True)
# Run the tasks
for task in flow.iflat_tasks():
task.start_and_wait()
assert task.status == task.S_DONE
flow.check_status()
assert flow.all_ok
# Optic does not support MPI with ncores > 1 hence we have to construct a manager with mpi_procs==1
shell_manager = fwp.manager.to_shell_manager(mpi_procs=1)
# Build optic task and register it
optic_task1 = flowtk.OpticTask(optic_input, nscf_node=bands_work.nscf_task, ddk_nodes=ddk_work,
manager=shell_manager)
flow.register_task(optic_task1)
flow.allocate()
flow.build_and_pickle_dump(abivalidate=True)
optic_task1.start_and_wait()
assert optic_task1.status == optic_task1.S_DONE
# Now we do a similar calculation but the dependencies are represented by
# strings with the path to the input files instead of task objects.
ddk_nodes = [task.outdir.has_abiext("1WF") for task in ddk_work]
#ddk_nodes = [task.outdir.has_abiext("DDK") for task in ddk_work]
print("ddk_nodes:", ddk_nodes)
assert all(ddk_nodes)
#nscf_node = bands_work.nscf_task
nscf_node = bands_work.nscf_task.outdir.has_abiext("WFK")
assert nscf_node
# This does not work yet
optic_task2 = flowtk.OpticTask(optic_input, nscf_node=nscf_node, ddk_nodes=ddk_nodes)
flow.register_task(optic_task2)
flow.allocate()
flow.build_and_pickle_dump(abivalidate=True)
assert len(flow) == 4
optic_task2.start_and_wait()
assert optic_task2.status == optic_task2.S_DONE
flow.check_status()
flow.show_status()
assert flow.all_ok
assert all(work.finalized for work in flow)
#assert flow.validate_json_schema()
# Test get_results
optic_task2.get_results()
"""
from __future__ import division, print_function, unicode_literals, absolute_import
import sys
import os
import numpy as np
import abipy.abilab as abilab
import abipy.data as data
optic_input = abilab.OpticInput(
broadening=0.002, # Value of the smearing factor, in Hartree
domega=0.0003, # Frequency mesh.
maxomega=0.3,
scissor=0.000, # Scissor shift if needed, in Hartree
tolerance=0.002, # Tolerance on closeness of singularities (in Hartree)
num_lin_comp=6, # Number of components of linear optic tensor to be computed
lin_comp=(11, 12, 13, 22, 23, 33), # Linear coefficients to be computed (x=1, y=2, z=3)
num_nonlin_comp=0 # Number of components of nonlinear optic tensor to be computed
#nonlin_comp=(123, 222),
)
global_vars = dict(
istwfk="*1",
paral_kgb=0,
ecut=8,
nstep=200,
diemac=12,
ixc=7,
chksymbreak=0,
#accesswff=3
