vibespin.scripts.ising package

Submodules

scripts.ising.coarsening_analysis module

Supplementary coarsening analysis for the 2D Ising model.

Precomputes three data sets used by the Correlation_and_Coarsening notebook:

Quench-depth sensitivity: Multiple quench temperatures, multi-seed median R_xi(t) at each, confirming that the growth exponent n = 1/2 is independent of quench depth while the prefactor depends on temperature.
Equilibrium crossover: Coarsening R_xi(t) trajectory on a small lattice long enough to saturate at the equilibrium correlation length xi_eq, demonstrating the bridge between non-equilibrium coarsening and equilibrium structure.
Stochastic ensemble: Multiple independent seeds at one quench temperature, storing per-seed traces to visualize run-to-run variability and the ensemble median + IQR.

scripts.ising.coarsening_analysis.main() → None[source]: Run the supplementary coarsening analysis.

scripts.ising.correlation_comparison module

Comparison of spin-spin correlation functions for the 2D Ising model. Analyzes correlation behavior in ferromagnetic, critical, and paramagnetic phases.

scripts.ising.correlation_comparison.main() → None[source]: Run the correlation comparison analysis.

scripts.ising.correlation_comparison.simulate_correlation(params: tuple[float, int, int, int, int]) → tuple[ndarray, ndarray][source]

Worker function: simulate and return the averaged correlation function at temperature T.

Parameters:: params (Tuple of (T, L, steps, eq_steps, sample_interval).)
Return type:: A tuple of (r, G_r) - radial distances and averaged correlations.

scripts.ising.correlation_divergence module

Analysis of correlation length divergence in the 2D Ising model. Extracts the critical exponent nu by fitting correlation lengths near Tc.

scripts.ising.correlation_divergence.get_correlation_length(params: tuple[float, int, int, int, int]) → tuple[float, float][source]

Simulate and extract correlation length xi for a given temperature.

Parameters:: params (Tuple of (T, L, steps, eq_steps, sample_interval).)
Return type:: A tuple of (T, xi).

scripts.ising.correlation_divergence.run_divergence_analysis() → None[source]: Run parallel simulation to extract the critical exponent nu from xi(T) divergence.

scripts.ising.diag_eq_traces module

Diagnostic tool for visualizing two-start equilibration traces.

This script runs simultaneous random-start and ordered-start simulations of the 2D Ising model at multiple representative temperatures (deep ordered, critical, and paramagnetic). It identifies seeds that lead to metastable stuck states at low temperatures and generates a multi-panel visualization of the magnetization trajectories.

Results are saved to results/ising/diagnostic_eq_traces_L<size>.png.

scripts.ising.diag_eq_traces.PALETTE: dict[str, str] = {'convergence': '#888888', 'ordered': '#D65F5F', 'random': '#4878CF'}: Color palette for traces.

scripts.ising.diag_eq_traces.TC_ISING: float = np.float64(2.269185314213022): Exact Onsager critical temperature for the 2D nearest-neighbour Ising model.

scripts.ising.diag_eq_traces.collect_trace(*, size: int, temp: float, seed: int, init_state: str, n_chunks: int, chunk_size: int) → ndarray[source]

Run a simulation and collect the magnetization trace.

Parameters:

size (int) – Linear lattice size L.
temp (float) – Temperature T.
seed (int) – RNG seed.
init_state (str) – Initial state (‘random’ or ‘ordered’).
n_chunks (int) – Number of measurement chunks.
chunk_size (int) – Sweeps per chunk.

Returns:

Array of magnetization values at each sweep.

Return type:

np.ndarray

scripts.ising.diag_eq_traces.main() → None[source]: Execute the equilibration diagnostic and generate plots.

scripts.ising.measure_z module

Measure the dynamical critical exponent z for Metropolis and Wolff algorithms.

This script runs simulations of the 2D Ising model at the critical temperature Tc for various lattice sizes L. It calculates the integrated autocorrelation time tau_int and fits tau_int ~ L^z to extract the dynamic exponent z.

Results are saved to results/ising/dynamic_exponent_z.npz.

scripts.ising.measure_z.TC_ISING: float = np.float64(2.269185314213022): Exact Onsager critical temperature for the 2D nearest-neighbour Ising model.

scripts.ising.measure_z.main() → None[source]: Execute the dynamical critical exponent measurement sweep.

scripts.ising.ordering_evolution module

Domain ordering evolution visualisation for the 2D Ising model.

Quenches from a disordered state to T < T_c and records the spin configuration at multiple time steps, plotting spin configurations, structure factors, and radially averaged correlation functions G(r).

scripts.ising.ordering_evolution.main() → None[source]: Run the simulation and generate a multi-row domain ordering figure.

scripts.ising.ordering_kinetics module

Ordering kinetics analysis for the 2D Ising model.

Starting from a disordered state, quenches to T < T_c and tracks the growth of domain size R(t) and the evolution of domain boundaries.

scripts.ising.ordering_kinetics.main() → None[source]: Run the Ising ordering kinetics simulation.

scripts.ising.temperature_sweep module

Standardized temperature sweep for the 2D Ising model. Calculates and plots magnetization, energy, susceptibility, and specific heat.

scripts.ising.temperature_sweep.main() → None[source]: Execute the temperature sweep and generate standardized 4-panel plots.

scripts.ising.wolff_efficiency module

Wolff cluster algorithm efficiency demonstration for the 2D Ising model.

Compares integrated autocorrelation time (tau_int), independent samples per second (ISS), mean cluster size fraction, and susceptibility between the Metropolis checkerboard and Wolff cluster algorithms across a temperature range centred on the critical point T_c ~= 2.269.

Results are saved to results/ising/wolff_efficiency.npz for notebook re-use and results/ising/wolff_efficiency.png as a 4-panel figure.

scripts.ising.wolff_efficiency.TC_ISING: float = np.float64(2.269185314213022): Exact Onsager critical temperature for the 2D nearest-neighbour Ising model.

scripts.ising.wolff_efficiency.main() → None[source]

Execute the Wolff efficiency comparison sweep and save figure and data.

Runs _measure_efficiency_point in parallel across the requested temperature range, then writes a 4-panel PNG and an .npz data file to --output-dir. The .npz is consumed by Wolff_Efficiency.ipynb to avoid re-running the sweep.