SPDX-License-Identifier: AGPL-3.0-or-later¶

Commercial license available¶

© Concepts 1996–2026 Miroslav Šotek. All rights reserved.¶

© Code 2020–2026 Miroslav Šotek. All rights reserved.¶

ORCID: 0009-0009-3560-0851¶

Contact: www.anulum.li | protoscience@anulum.li¶

scpn-quantum-control — Kuramoto visualisation layer¶

Kuramoto Visualisation Layer¶

scpn_quantum_control.accel.kuramoto_visualisation (re-exported from the scpn_quantum_control.kuramoto facade under the visualisation capability group) renders the standard diagnostics for coupled-phase-oscillator trajectories directly from the shipped trajectory and observable surfaces. Four renderers are provided:

Renderer	Input	What it shows
`phase_raster`	`(T, N)` trajectory	The phase of every oscillator over time as an oscillator-by-time raster with a cyclic colour map — travelling waves, locking and incoherence at a glance.
`order_parameter_timeseries`	`(T, N)` trajectory	The Kuramoto order parameter `r(t)`, read from the same complex mean field as `order_parameter`.
`chimera_snapshot`	`(T, N)` trajectory + community partition	The per-community order parameters over time (`community_order_parameters`), the title annotated with the `chimera_index`.
`network_phase_embedding`	`(N,)` phase snapshot + `(N, N)` coupling	A single-time phase snapshot on a circular node layout with the coupling drawn as edges.

Optional dependency¶

Matplotlib is an optional dependency, declared as the viz extra:

pip install 'scpn-quantum-control[viz]'

It is imported lazily, so importing the accel or kuramoto facade never requires matplotlib; calling a renderer without it raises an ImportError naming the install command. The renderers do not compute anything of their own — they reuse order_parameter, community_order_parameters and chimera_index, so the curves and annotations are exactly the shipped diagnostics.

Usage¶

Every renderer draws into a caller-supplied axis when given (so panels compose into one figure) or creates its own otherwise, returns the axis, and never calls show — the caller owns display and saving.

import numpy as np
import matplotlib.pyplot as plt

from scpn_quantum_control.kuramoto import (
    kuramoto_rk4_trajectory,
    phase_raster,
    order_parameter_timeseries,
    chimera_snapshot,
    network_phase_embedding,
)

rng = np.random.default_rng(0)
count = 24
coupling = np.full((count, count), 0.8)
np.fill_diagonal(coupling, 0.0)
theta0 = rng.uniform(-np.pi, np.pi, count)
omega = rng.normal(0.0, 1.0, count)
phases = kuramoto_rk4_trajectory(theta0, omega, coupling, dt=0.02, n_steps=400)  # (401, 24)

figure, axes = plt.subplots(2, 2, figsize=(11, 8))
phase_raster(phases, ax=axes[0, 0])
order_parameter_timeseries(phases, ax=axes[0, 1])
chimera_snapshot(phases, [np.arange(0, 12), np.arange(12, 24)], ax=axes[1, 0])
network_phase_embedding(phases[-1], coupling, ax=axes[1, 1])
figure.tight_layout()
figure.savefig("kuramoto_dashboard.png", dpi=150)

The times keyword sets the horizontal axis for the trajectory renderers; when omitted the integer step index is used.