reservoirpy.datasets.doublescroll#

reservoirpy.datasets.doublescroll(n_timesteps, r1=1.2, r2=3.44, r4=0.193, ir=4.5e-05, beta=11.6, x0=[0.37926545, 0.058339, -0.08167691], h=0.25, **kwargs)[source]#

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

\[\begin{split}\frac{\mathrm{d}V_1}{\mathrm{d}t} &= \frac{V_1}{R_1} - \frac{\Delta V}{R_2} - 2I_r \sinh(\beta\Delta V) \\ \frac{\mathrm{d}V_2}{\mathrm{d}t} &= \frac{\Delta V}{R_2} +2I_r \sinh(\beta\Delta V) - I\\ \frac{\mathrm{d}I}{\mathrm{d}t} &= V_2 - R_4 I\end{split}\]

where \(\Delta V = V_1 - V_2\).

Parameters:

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

  • r1 (float, default to 1.2) – \(R_1\) parameter of the system.

  • r2 (float, default to 3.44) – \(R_2\) parameter of the system.

  • r4 (float, default to 0.193) – \(R_4\) parameter of the system.

  • ir (float, default to 2*2e.25e-5) – \(I_r\) parameter of the system.

  • beta (float, default to 11.6) – \(\beta\) parameter of the system.

  • x0 (array-like of shape (3,), default to [0.37926545, 0.058339, -0.08167691]) – Initial conditions of the system.

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

Returns:

Multiscroll attractor timeseries.

Return type:

array of shape (n_timesteps, 3)

References