Generate colors of noise in Python

Question

I would like to use Python to generate different colors of noise, just like Wikipedia mentions : https://en.wikipedia.org/wiki/Colors_of_noise.

For example, White, Pink, Brownian, Blue and Violet noise. And would like to have similar spectrums just like the website.

It would be a great help if I could just adjust a few parameters to get it done. Any links or tips would be very appreciated! Thanks a lot.

Answer 1

Let's use numpy to compute the noise and matplotlib to plot the results

import numpy as np
import matplotlib.pyplot as plt

def plot_spectrum(s):
    f = np.fft.rfftfreq(len(s))
    plt.loglog(f, np.abs(np.fft.rfft(s)))

This is a good use case for a python decorator

def noise_psd(N, psd = lambda f: 1):
        X_white = np.fft.rfft(np.random.randn(N));
        S = psd(np.fft.rfftfreq(N))
        # Normalize S
        S = S / np.sqrt(np.mean(S**2))
        X_shaped = X_white * S;
        return np.fft.irfft(X_shaped);

def PSDGenerator(f):
    return lambda N: noise_psd(N, f)

@PSDGenerator
def white_noise(f):
    return 1;

@PSDGenerator
def blue_noise(f):
    return np.sqrt(f);

@PSDGenerator
def violet_noise(f):
    return f;

@PSDGenerator
def brownian_noise(f):
    return 1/np.where(f == 0, float('inf'), f)

@PSDGenerator
def pink_noise(f):
    return 1/np.where(f == 0, float('inf'), np.sqrt(f))

The function PSDGenrator takes as input a function and returns another function that will produce a random signal with the power spectrum shaped accordingly to the given function.

The line S = S / np.sqrt(np.mean(S**2)) makes sure that the colored noise will preserve the energy of the white noise.

Let's test

plt.figure(figsize=(8, 8))
for G in [brownian_noise, pink_noise, white_noise, blue_noise, violet_noise]:
    plot_spectrum(G(2**14))
plt.legend(['brownian', 'pink', 'white', 'blue', 'violet'])
plt.ylim([1e-3, None]);

Answer 2

There is a library to work with colored noise in python

https://pypi.org/project/colorednoise/

!pip install colorednoise
import colorednoise as cn
from matplotlib import pylab as plt

#input values
beta = 0         # the exponent: 0=white noite; 1=pink noise;  2=red noise (also "brownian noise")
samples = 2**16  # number of samples to generate (time series extension)

#Deffing some colores
A = cn.powerlaw_psd_gaussian(beta, samples)

#Ploting first subfiure
plt.plot(A, color='black', linewidth=1)
plt.title('Colored Noise for ?='+str(beta))
plt.xlabel('Samples (time-steps)')
plt.ylabel('Amplitude(t)', fontsize='large')
plt.xlim(1,5000)
plt.show()