reservoirpy.datasets.multiscroll#

reservoirpy.datasets.multiscroll(
n_timesteps: int,
a: float = 40.0,
b: float = 3.0,
c: float = 28.0,
x0: list | ndarray = [-0.1, 0.5, -0.6],
h: float = 0.01,
**kwargs,
) → ndarray[source]#

Double scroll attractor timeseries [10] [11], a particular case of multiscroll attractor timeseries.

\[\begin{split}\frac{\mathrm{d}x}{\mathrm{d}t} &= a(y - x) \\ \frac{\mathrm{d}y}{\mathrm{d}t} &= (c - a)x - xz + cy \\ \frac{\mathrm{d}z}{\mathrm{d}t} &= xy - bz\end{split}\]
Parameters:

  • n_timesteps (int) – Number of timesteps to generate.

  • a (float, default to 40.0) – \(a\) parameter of the system.

  • b (float, default to 3.0) – \(b\) parameter of the system.

  • c (float, default to 28.0) – \(c\) parameter of the system.

  • x0 (array-like of shape (3,), default to [-0.1, 0.5, -0.6]) – Initial conditions of the system.

  • h (float, default to 0.01) – Time delta between two discrete timesteps.

Return type:

ndarray

Examples

>>> from reservoirpy.datasets import multiscroll
>>> timeseries = multiscroll(1000)
>>> timeseries.shape
(1000, 3)
../../_images/reservoirpy-datasets-multiscroll-1.png
Returns:

Multiscroll attractor timeseries.

Return type:

array of shape (n_timesteps, 3)

Parameters:

References