Example Usage¶

The following example produce 1000 samples of brownian noise starting in value 10 (start=10). The noise is produced by integration of white gaussian noise (source=”gaussian”) with standard deviation of 1 (std=1). To keep in noise in some reasonable range, it is used 10% leaky integration (leak=0.1).

import signalz
x = signalz.brownian_noise(1000, leak=0.1, start=10, std=1, source="gaussian")

Function Documentation¶

signalz.generators.brownian_noise.brownian_noise(n, leak=0.0, start=0, std=1.0, source='gaussian')[source]¶

This function produces Browninan noise.

Args:

• n - length of the output data (int) - how many samples will be on output

Kwargs:

• leak - leakage during integration (float), this should prevent signal from wander off. Possible value is 0 <= leak < 1
• start - offset on the start (float)
• std - standard deviation of source white noise (float), in case of uniform distribution it is the difference between min and max value.
• source - distribution of source white noise (str). Options are gaussian or uniform

Returns:

• vector of values representing the noise (1d array)