Data Generator Chaos Lorenz

Lorenz strange attractor. Generates three chaotic outputs (x, y, z) from a system of three coupled differential equations discovered by Edward Lorenz in 1963. The trajectory traces a butterfly-shaped path that never exactly repeats, producing complex, evolving modulation signals. Outputs are normalized to approximately 0..+1.

A chaotic attractor is a mathematical system whose output is deterministic but unpredictable over time. Unlike random noise, the signals are smooth and continuous, making them ideal for organic, never-repeating modulation of parameters like filters, panning, or effects.

Settings

on

Activates or deactivates the module.

x

Chaotic X output, normalized to approximately 0..+1.

y

Chaotic Y output, normalized to approximately 0..+1.

z

Chaotic Z output, normalized to approximately 0..+1.

sigma

Rate of rotation around the attractor lobes. Classic value: 10.

rho

Drives the folding behavior. Higher values increase the attractor size. Classic value: 28. Below ~24.74 the system converges to a fixed point.

beta

Contraction rate along the Z axis. Classic value: 8/3 (2.667).

speed

Integration speed. Higher values make the attractor evolve faster. Default is 5.

reset

Resets the attractor state to initial conditions.

See also

• Generators
• Data Generator Turing Machine
• Data Generator Pattern
• Data Generator Sequenced Lines
• Data Generator Random Urn
• Data Generator Burst
• Data Generator Sequenced Triggers
• Data Generator ADSR
• Data Generator Sequenced Switches
• Data Generator LFO
• Data Generator Sequenced Steps
• Data Generator Random
• Data Generator Sequenced Points Curve
• Data Generator Ramp
• Data Generator Random Pattern Array (SCRIPT)
• Data Generator Chaos Rossler
• Data Generator Chaos Lorenz
• Data Generator Perlin Noise
• Data Generator Euclidean Rhythm
• Data Generator Grain
• Data Generator Random Walk
• Data Probability Gate