Configuration

All simulation parameters are set through frozen dataclasses. Once created, they're immutable — no accidental mutations during a run.

SimulationConfig

The top-level config that controls everything:

from mcising import (
    SimulationConfig, LatticeConfig, AdaptiveConfig,
    Algorithm, ExecutionMode, LatticeType,
)

config = SimulationConfig(
    lattice=LatticeConfig(
        lattice_type=LatticeType.SQUARE,  # square, triangular, honeycomb, cubic, chain
        size=32,                           # linear extent L
        j1=1.0,                            # nearest-neighbor coupling
        j2=0.0,                            # next-nearest-neighbor coupling
        j3=0.0,                            # third-nearest-neighbor coupling
        h=0.0,                             # external magnetic field
    ),
    algorithm=Algorithm.METROPOLIS,        # metropolis, wolff, swendsen_wang
    seed=42,                               # deterministic RNG seed
    temperatures=(3.0, 2.269, 1.5),        # temperature points
    n_sweeps=1000,                         # measurement sweeps per temperature
    n_thermalization=100,                  # warmup sweeps
    measurement_interval=10,               # measure every N sweeps
    compute_correlation=False,             # compute C(r) per temperature
    adaptive=AdaptiveConfig(enabled=False),# adaptive thermalization
    mode=ExecutionMode.COOLDOWN,           # cooldown, independent, parallel_tempering
    swap_interval=1,                       # sweeps between PT swaps
)

LatticeConfig

Parameter	Type	Default	Description
lattice_type	LatticeType	SQUARE	Lattice geometry
size	int	10	Linear extent L (must be >= 2)
j1	float	1.0	Nearest-neighbor coupling
j2	float	0.0	Next-nearest-neighbor coupling
j3	float	0.0	Third-nearest-neighbor coupling
h	float	0.0	External magnetic field

All coupling values must be finite. Cluster algorithms (Wolff, Swendsen-Wang) require j2=0, j3=0, h=0.

Algorithm constraints

Algorithm	J1	J2	J3	h	All lattices?
Metropolis	any	any	any	any	Yes
Wolff	any	0 only	0 only	0 only	Yes
Swendsen-Wang	any	0 only	0 only	0 only	Yes

Temperature specification

Temperatures must be positive and finite. For the cooldown mode, they're automatically sorted in descending order.

# Individual temperatures
temperatures=(3.0, 2.269, 1.5)

# Dense scan (via numpy)
import numpy as np
temperatures=tuple(np.linspace(1.5, 3.5, 50))

Defaults

Parameter	Default
lattice_type	SQUARE
size	10
j1	1.0
j2, j3, h	0.0
algorithm	METROPOLIS
seed	42
n_sweeps	1000
n_thermalization	100
measurement_interval	10
mode	COOLDOWN
swap_interval	1

See the API Reference for complete documentation of all parameters.