Data

Tools

Indexes

Applications

Experiments

Open Source Science

colorednoise

Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)

https://github.com/felixpatzelt/colorednoise

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 5 committers (40.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.4%) to scientific vocabulary

Keywords

correlations noise-generator power-laws python python2 python3 time-series
Last synced: 6 months ago · JSON representation

Repository

Python package to generate Gaussian (1/f)**beta noise (e.g. pink noise)

Basic Info
  • Host: GitHub
  • Owner: felixpatzelt
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 2.48 MB
Statistics
  • Stars: 212
  • Watchers: 3
  • Forks: 22
  • Open Issues: 2
  • Releases: 7
Topics
correlations noise-generator power-laws python python2 python3 time-series
Created over 8 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog License

README.rst 

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 `_


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()

Owner

  • Name: Felix Patzelt
  • Login: felixpatzelt
  • Kind: user
  • Company: @moia-dev

Data Science and Interdisciplinary Physics - Socioeconomic and Neural Systems

GitHub Events

Total
  • Watch event: 17
  • Fork event: 2
Last Year
  • Watch event: 17
  • Fork event: 2

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 39
  • Total Committers: 5
  • Avg Commits per committer: 7.8
  • Development Distribution Score (DDS): 0.179
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Felix Patzelt f****x@n****e 32
Alex Spaeth a****h@u****u 4
charles c****s@a****o 1
Rudolf A Braun r****4@g****m 1
Onno Eberhard o****d 1
Committer Domains (Top 20 + Academic)
ae.studio: 1 ucsc.edu: 1 neuro.uni-bremen.de: 1

Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 7
  • Total pull requests: 12
  • Average time to close issues: 11 days
  • Average time to close pull requests: 10 days
  • Total issue authors: 7
  • Total pull request authors: 6
  • Average comments per issue: 4.14
  • Average comments per pull request: 1.67
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • vbschettino (1)
  • rbracco (1)
  • richgumy (1)
  • tomato18463 (1)
  • yxiao1996 (1)
  • terrytykuo (1)
  • paulbrodersen (1)
Pull Request Authors
  • felixpatzelt (5)
  • i-aki-y (1)
  • onnoeberhard (1)
  • atspaeth (1)
  • charlesincharge (1)
  • RuABraun (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 16,458 last-month
  • Total docker downloads: 10
  • Total dependent packages: 9
  • Total dependent repositories: 21
  • Total versions: 7
  • Total maintainers: 1
pypi.org: colorednoise

Generate Gaussian (1/f)**beta noise (e.g. pink noise)

  • Versions: 7
  • Dependent Packages: 9
  • Dependent Repositories: 21
  • Downloads: 16,458 Last month
  • Docker Downloads: 10
Rankings
Dependent packages count: 1.6%
Dependent repos count: 3.2%
Docker downloads count: 3.8%
Average: 4.7%
Downloads: 5.3%
Stargazers count: 5.6%
Forks count: 8.9%
Maintainers (1)
felixpatzelt
Last synced: 6 months ago

Dependencies

setup.py pypi
  • numpy >=1.17.0