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()