# Colorednoise

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

```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()
```
```# 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))
```
```# 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()
```
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