def observables(MCcalc, outputfi):
energy = MCcalc.energy
# nup = MCcalc.config.count("OH",0)[0]
# ndown = MCcalc.config.count("OH",1)[0]
# tot_pol = np.abs(nup - ndown)/6
grid = grid_1D(0.02, 0.02, 5.0)
g_r_OY = g_r(MCcalc.config.structure, "O", "Y", grid)
g_r_OZr = g_r(MCcalc.config.structure, "O", "Zr", grid)
g_r_YY = g_r(MCcalc.config.structure, "Y", "Y", grid)
# energy2 = energy**2.0
# xparam = MCcalc.model.xparam(MCcalc.config)
outputfi.write(
"\t".join([str(observable)
for observable in [MCcalc.kT, energy]]) + "\n")
outputfi.flush()
scalar_obs = [MCcalc.kT, energy]
return obs_encode(scalar_obs, g_r_OY, g_r_OZr, g_r_YY)
import random as rand
import sys
from abics.mc import CanonicalMonteCarlo, grid_1D
from model_setup import *
if __name__ == "__main__":
L = 1.0
rdisc = 1.0 / 14.0
Ndisc = 20
dr = 0.01
maxstep = rdisc / 2
kT = 1
grid = grid_1D(dr, dr, L / 2.0)
config = HC_2D_config(L, Ndisc, rdisc)
model = HC_2D(maxstep)
config.prepare_ordered()
print(config.coors)
print(g_r(config, grid))
calc = CanonicalMonteCarlo(model, kT, config, grid=grid)
plot_fig(calc, 10000)
# calc.run(10000, observefunc=observables)
# print(config.coors)
"""calc = Canonical
J = -1.0
kT = abs(J) * 1.0
size = 10
# poscar = Poscar(HAp_config.structure)
# poscar.write_file("POSCAR.vasp")
################### model setup ###############################
# baseinput = VaspInput.from_directory("baseinput")
# energy_calculator = vasp_runner(base_input_dir="./baseinput",
# path_to_vasp="/home/i0009/i000900/src/vasp.5.3/vasp.spawnready.gamma",
# nprocs_per_vasp=nprocs_per_replica,
# comm=MPI.COMM_SELF, perturb=0.1)
energy_calculator = SolverBase()
model = dft_latgas(energy_calculator, save_history=True)
##############################################################
################### RXMC calculation #########################
kTs = kB * np.array([kTstart * kTstep**i for i in range(nreplicas)])
grid = grid_1D(dr, dr, maxr)
RXcalc = TemperatureRX_MPI(comm, CanonicalMonteCarlo, model, configs, kTs)
obs = RXcalc.run(eqsteps,
RXtrial_frequency,
sample_frequency,
observfunc=observables,
subdirs=True)
# RXcalc.reload()
obs = RXcalc.run(nsteps,
RXtrial_frequency,
sample_frequency,
observfunc=observables,
subdirs=True)
if comm.Get_rank() == 0: