Frustration_scan module#
Tools to scan frustration parameters.
- simplicial_kuramoto.frustration_scan.scan_frustration_parameters(simplicial_complex, alpha1=array([0., 0.34906585, 0.6981317, 1.04719755, 1.3962634, 1.74532925, 2.0943951, 2.44346095, 2.7925268, 3.14159265]), alpha2=array([0., 0.22222222, 0.44444444, 0.66666667, 0.88888889, 1.11111111, 1.33333333, 1.55555556, 1.77777778, 2.]), repeats=1, n_workers=4, t_max=200, n_t=1000, save=True, folder='./results/', filename='results.pkl', harmonic=False)[source]#
Scan frustration parameters alpha_1 and alpha_2.
- Parameters:
simplicial_complex (SimplicialComplex) – simplicial complex to use
alpha1 (array) – alpha1 values to scan
alpha2 (array) – alpha2 values to scan
repeats (int) – number of repeat of same point with random initial conditions
n_workers (int) – number of workers for multiprocessing
t_max (float) – integration time
n_t (int) – number of timepoints
save (bool) – save results in a picle
folder (str) – folder to save results
filename (str) – name of pickle file
harmonic (bool) – to use a harmonic alpha1 vector scaled by given alpha1
- Returns:
results of the scan
- simplicial_kuramoto.frustration_scan.proj_subspace(vec, subspace)[source]#
Project a list of vecs to a given subspace (from get_subspaces).
- simplicial_kuramoto.frustration_scan.get_projection_fit(Gsc, res, grad_subspace=None, curl_subspace=None, harm_subspace=None, n_min=0)[source]#
Project result on subspaces and compute linear fit.
- simplicial_kuramoto.frustration_scan.plot_lyapunov(path, filename='lyap.pdf', nolds_kwargs=None)[source]#
Compute and plot largest lyapunov exponents.
The computation is based on nolds package, and yse nolds_kwargs to configure it.
- simplicial_kuramoto.frustration_scan.plot_order_1d(path, filename, frac=0.5, eps=1e-05, with_std=False)[source]#
Plot order and projection with fixed alpha1.