colorednoise.py

Generate Gaussian distributed noise with a power law spectrum with arbitrary exponents.

An exponent of two corresponds to brownian noise. Smaller exponents yield long-range correlations, i.e. pink noise for an exponent of 1 (also called 1/f noise or flicker noise).

Based on the algorithm in:

Timmer, J. and Koenig, M.:
On generating power law noise. 
Astron. Astrophys. 300, 707-710 (1995)

Further reading: Colors of noise on Wikipedia <//en.wikipedia.org/wiki/Colors_of_noise>_

Installation

pip install colorednoise

Dependencies

- Python >= 2.7 or >= 3.6
- NumPy

Other Python versions were not tested, but are likely to work.

Example

.. code:: python

import colorednoise as cn
beta = 1 # the exponent
samples = 2**18 # number of samples to generate
y = cn.powerlaw_psd_gaussian(beta, samples)

# optionally plot the Power Spectral Density with Matplotlib
#from matplotlib import mlab
#from matplotlib import pylab as plt
#s, f = mlab.psd(y, NFFT=2**13)
#plt.loglog(f,s)
#plt.grid(True)
#plt.show()