"""
Ordering kinetics analysis for the 2D q-state Clock model.
Quenches from a disordered state to T < T_c and records length scale
growth and vortex density decay over time.
"""
from __future__ import annotations
import argparse
import logging
import numpy as np
from tqdm import tqdm
from models.clock_model import ClockSimulation
from utils.cli_helpers import parse_args_compat
from utils.physics_helpers import compute_kinetics_metrics, power_fit
from utils.system_helpers import (
_BAR_FORMAT,
ensure_results_dir,
plot_ordering_kinetics,
setup_logging,
)
[docs]
def main() -> None:
"""Run the Clock ordering kinetics simulation."""
parser = argparse.ArgumentParser(description='2D Clock Model Ordering Kinetics Analysis')
parser.add_argument('--size', type=int, default=256, help='Linear lattice size L')
parser.add_argument('--temp', type=float, default=0.1, help='Quench temperature T')
parser.add_argument('--q', type=int, default=6, help='Number of clock states')
parser.add_argument('--aniso', type=float, default=0.5, help='Anisotropy strength A')
parser.add_argument('--max-steps', type=int, default=1000, help='Total MC steps')
parser.add_argument('--samples', type=int, default=15, help='Number of measurement points')
parser.add_argument('--fit-min', type=int, default=20, help='Min step for power-law fit')
parser.add_argument('--output-dir', type=str, default='results/clock', help='Output directory')
parser.add_argument('--log-file', type=str, default=None, help='Optional log file path')
parser.add_argument('--verbose', action='store_true', help='Enable verbose logging')
args = parse_args_compat(parser)
# Configure logging
log_level = logging.DEBUG if args.verbose else logging.INFO
logger = setup_logging(level=log_level, log_file=args.log_file)
L = args.size
T = args.temp
Q = args.q
A = args.aniso
logger.info(f'Clock ordering kinetics analysis (L={L}, T={T:.3f}, q={Q}, A={A})')
step_targets = np.unique(np.logspace(0, np.log10(args.max_steps), num=args.samples).astype(int))
sim = ClockSimulation(size=L, temp=T, q=Q, A=A, update='random')
N_data = len(step_targets)
t = np.zeros(N_data)
R_sk = np.zeros(N_data)
R_xi = np.zeros(N_data)
v_dens = np.zeros(N_data)
current_step = 0
for i, target in enumerate(tqdm(step_targets, bar_format=_BAR_FORMAT, desc='Simulating')):
steps_to_run = int(target) - current_step
for _ in range(steps_to_run):
sim.step()
current_step = int(target)
metrics = compute_kinetics_metrics(sim=sim)
t[i] = float(current_step)
R_sk[i] = metrics['R_sk']
R_xi[i] = metrics['xi']
v_dens[i] = sim._get_vortex_density()
logger.debug(f't={current_step}: R_sk={R_sk[i]:.2f}, xi={R_xi[i]:.2f}, n_v={v_dens[i]:.4f}')
# Power law fits
fit_mask = t >= args.fit_min
exponents = {}
prefactors = {}
for key, data in [('R_sk', R_sk), ('xi', R_xi), ('third', v_dens)]:
exp, pre = power_fit(t_arr=t, y_arr=data, mask=fit_mask)
exponents[key], prefactors[key] = exp, pre
if exp:
label = 'Growth' if key != 'third' else 'Decay'
logger.info(f'{key} {label} exponent: {exp:.3f}')
plot_ordering_kinetics(
t=t,
R_sk=R_sk,
R_xi=R_xi,
third_metric=v_dens,
third_metric_label='Vortex Density $n_v(t)$',
exponents=exponents,
prefactors=prefactors,
fit_mask=fit_mask,
title=f'2D {Q}-state Clock Ordering Kinetics - $T = {T}$, $L = {L}$, $A = {A}$',
filename='ordering_kinetics.png',
directory=ensure_results_dir(directory=args.output_dir),
left_title='Phase Ordering Dynamics',
right_title='Vortex Decay',
)
if __name__ == '__main__':
main()