import colorednoise as cn
import matplotlib.pyplot as plt
from matplotlib import mlab

betas = [-2, -1, 0, 1, 2]
colors = ['violet', 'blue', 'gray', 'pink', 'darkred']
samples = 100*44100

plt.style.use('default')
plt.rc('font', family='serif', size=14)
plt.rc('text', usetex=True)

fig = plt.figure(dpi=300)
plt.annotate(r'\textit{The Colors of Noise}', (1300, 42), fontsize=16)
nfft=2**21

for beta, color in zip(betas, colors):
    y = cn.powerlaw_psd_gaussian(beta, samples)
    s, f = mlab.psd(y, NFFT=nfft)
    plt.semilogx(2e5*f, 4 + 10*np.log10(s/s[4100:4200].mean())
        , color=color, lw=0.5)
    plt.xlabel('Frequency (Hz)')
    plt.ylabel('Power Spectral Density (dB)')
    plt.xlim([1e3, 2e5])
    plt.ylim([-70, 56])
plt.tight_layout()

if fig.dpi >= 300:
    plt.savefig('../fig/The_Colors_of_Noise.svg')