def main(system_params=None):
from boost.mpi import world, broadcast
from pylada.vasp.extract import Extract, ExtractGW
from pylada.vasp.specie import U, nlep
from pylada.vasp import Vasp
import os
from os import path
from .nlep import Objective
from pylada.vasp.nlep.postprocessing import find_best, load_test, prepare_analog_fit
print("mpi rank %d of %d" % (world.rank, world.size))
# Things which need to be put into dictionary of input script.
# global_dict = { "Extract": Extract, "ExtractGW": ExtractGW, "Vasp": Vasp, "nlep": nlep, "U": U }
# read file governing run. other input files should be generic
run_input = load_run_input()
run_control = RunControl(run_input)
load_from_analogs = run_input.load_from_analogs
# create systems
from .systems import setup_systems
systems = setup_systems(run_input, system_params)
if (run_input.nbjobs == -1):
run_control.nbjobs = world.size
assert world.size >= run_control.nbjobs, "Too few procs granted, or too many jobs requested."
job_comm = world
vasp_comm = world
color = world.rank % run_control.nbjobs
job_comm = world.split(color)
vasp_comm = job_comm
if (job_comm.size > 1):
vasp_comm = job_comm.split(job_comm.rank)
create_directories(run_control, systems, job_comm, color)
# barrier to be sure all necessary directories exist
world.barrier()
# creates objective functions from systems
for s in systems.systems:
print(s.outdir)
s.objective = Objective(s.input.vasp, s.input.dft_in, s.input.gw_in,
outdir=s.outdir, comm=vasp_comm, units=run_input.units)
s.evals_file = file("%s/evals" % s.outdir, "w")
systems.evals_file = file("%s/evals" % run_control.outdir, "w")
# setup correspondence between vector "x" and list of species (ie at least
# one subpart shared by > 1 systems):
systems.setup_species()
# barrier to be sure all necessary directories exist
world.barrier()
# only serial vasp so far, but lmmin is a parallel fitter
if (job_comm.rank != 0 and run_input.optimizer != "lmmin"):
print("rank %d superfluous, returning; beware barrier" % world.rank)
return
print("world rank %d job %d local rank %d working dir %s" % (world.rank, color, job_comm.rank, run_control.outdir))
job_main(job_comm, run_input, systems, vasp_comm)
def main():
from boost.mpi import world
from scipy.optimize import fmin as scipy_simplex
from pylada.vasp import Extract, ExtractGW, Vasp, Specie
from pylada.vasp.specie import nlep as nlep_parameters, U as u_parameters
from pylada.vasp.incar import Standard, NBands
from sys import exit
indir = "SnO2"
dft_in = Extract(directory=indir, comm=world)
dft_in.OUTCAR = "OUTCAR_pbe"
dft_in.CONTCAR = "POSCAR"
gw_in = ExtractGW(directory=indir, comm=world)
gw_in.OUTCAR = "OUTCAR_gw"
gw_in.CONTCAR = "POSCAR"
# Creates species with nlep parameters to optimize
species = Specie\
(
"Sn",
path="pseudos/Sn",
U=[nlep_parameters(type="Dudarev", l=i, U0=0e0) for i in ["s", "p", "d"]]
),\
Specie\
(
"O",
path="pseudos/O",
U=[nlep_parameters(type="Dudarev", l=i, U0=0e0) for i in ["s", "p"]]
)
# add U to Sn atoms.
species[0].U.append( u_parameters(type="Dudarev", U=2e0, l=2) )
# creates vasp launcher
vasp = Vasp\
(
kpoints = lambda x: "Automatic generation\n0\ngamma\n6 6 10\n0 0 0",
precision = "accurate",
smearing = "bloechl",
ediff = 1e-5,
relaxation = "ionic",
encut = 1, # uses ENMAX * 1, which is VASP default
species = species
)
# adds some extra parameters.
vasp.nbands = Standard("NBANDS", 64)
vasp.lorbit = Standard("LORBIT", 10)
vasp.npar = Standard("NPAR", 2)
vasp.lplane = Standard("LPLANE", ".TRUE.")
vasp.addgrid = Standard("ADDGRID", ".TRUE.")
del vasp.fftgrid
# creates objective function.
objective = Objective(vasp, dft_in, gw_in)
x0, f0, iter, funcalls, warnflag = scipy_simplex(objective, objective.x, maxfun=150, full_output=1, xtol=0.2)
world.barrier()
if world.rank == 0:
print "minimum value:", f0
print "for: ", x0 * units
print "after %i iterations and %i function calls." % (iter, funcalls)
print "with warning flag: ", warnflag
print final(x0)