def gajobs(path, inputpath="input.py"):
from copy import deepcopy
import IPython.ipapi
from pylada.ga.escan.elemental.functional import Darwin as Functional
from pylada.ga.escan.elemental.evaluator import Dipole as Evaluator
from pylada.ga.escan.elemental import LayeredConverter as Converter
from pylada.jobs import JobFolder
from pylada.escan import read_input
# reads input file.
input = read_input(inputpath)
jobfolder = JobFolder()
for scale in input.scales:
for p in input.periods:
for trial in input.trials:
supercell = input.supercell(p)
escan = deepcopy(input.escan)
escan.fft_mesh = input.fftmesh(supercell)
escan.vff.lattice.scale = scale
converter = Converter(supercell, lattice=escan.vff.lattice)
evaluator = Evaluator(converter=converter, escan=escan, **input.kwargs)
evaluator._outdir.envvar = "$SCRATCH"
functional = Functional(evaluator)
functional.popsize = input.population_size
functional.max_gen = input.max_generations
functional.rate = input.offspring_rate
functional.mean_conc = input.mean_conc
functional.stddev_conc = input.stddev_conc
functional.pools = input.pools
gajob = jobfolder / "scale_{0:.2f}/period_{1}/trial_{2}".format(scale, p, trial)
gajob.functional = functional
ip = IPython.ipapi.get()
ip.user_ns["current_jobfolder"] = jobfolder
ip.magic("savejobs " + path)
