causalcompass.datasets.nonstationary.simulate_nonstationary_lorenz_96

causalcompass.datasets.nonstationary.simulate_nonstationary_lorenz_96(p, T, F=10.0, delta_t=0.1, sd=0.1, noise_std=2.0, mean_log_sigma=2.5, burn_in=1000, seed=0)[source]

Generate Lorenz-96 data with time-varying noise variance.

Parameters:

  • p (int) – Number of variables

  • T (int) – Number of time points

  • F (float, default 10.0) – Forcing parameter

  • delta_t (float, default 0.1) – Time step for ODE solver

  • sd (float, default 0.1) – Noise standard deviation

  • noise_std (float, default 2.0) – Standard deviation of the GP

  • mean_log_sigma (float, default 2.5) – Mean of the GP

  • burn_in (int, default 1000) – Burn-in period

  • seed (int, default 0) – Random seed

Returns:

(data, GC, sigma_t)— time series array of shape (T, p), ground-truth causal graph of shape (p, p), and time-varying noise scaling factor of shape (T, p).

Return type:

tuple