https://github.com/felixpatzelt/colorednoise

Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)
https://github.com/felixpatzelt/colorednoise

correlations noise-generator power-laws python python2 python3 time-series

Last synced: 7 days ago
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Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)

• Host: GitHub
• URL: https://github.com/felixpatzelt/colorednoise
• Owner: felixpatzelt
• License: mit
• Created: 2017-09-23T16:43:02.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-10-09T21:03:34.000Z (about 1 year ago)
• Last Synced: 2024-11-06T03:48:02.133Z (15 days ago)
• Topics: correlations, noise-generator, power-laws, python, python2, python3, time-series
• Language: Python
• Size: 2.48 MB
• Stars: 195
• Watchers: 4
• Forks: 20
• Open Issues: 2
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.txt

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README

        
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 >= 3.6.15

	- NumPy >= 1.17.0

	

Older Python 3 versions were not tested, but are likely to work.

For Python 2 please use colorednoise version 1.x.

Examples

--------

.. 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()

	

	

.. code:: python

	# generate several time series of independent indentically distributed variables 

	# repeat the simulation of each variable multiple times

	import colorednoise as cn

	n_repeats   = 10   # repeat simulatons

	n_variables = 5    # independent variables in each simulation

	timesteps   = 1000 # number of timesteps for each variable

	y = cn.powerlaw_psd_gaussian(1, (n_repeats, n_variables, timesteps))

	

	# the expected variance of for each variable is 1, but each realisation is different

	print(y.std(axis=-1))

	

.. code:: python

	# generate a broken power law spectrum: white below a frequency of 

	import colorednoise as cn

	y = cn.powerlaw_psd_gaussian(1, 10**5, fmin=.05)

	s, f = mlab.psd(y, NFFT=2**9)

	#plt.loglog(f,s)

	#plt.grid(True)

	#plt.show()