Matplotlib PSD Plotting

Introduction

This tutorial will guide you through the process of plotting the Power Spectral Density (PSD) using the Matplotlib library in Python. The PSD is a plot commonly used in the field of signal processing. NumPy has many useful libraries for computing a PSD, and we will show a few examples of how this can be accomplished and visualized with Matplotlib.

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Skills Graph

Plot a basic PSD

First, we will plot a basic PSD using random data. We will create a time series, add noise, and then plot the PSD using the psd function from the matplotlib.mlab library.

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab

np.random.seed(19680801)
dt = 0.01
t = np.arange(0, 10, dt)
nse = np.random.randn(len(t))
r = np.exp(-t / 0.05)
cnse = np.convolve(nse, r) * dt
cnse = cnse[:len(t)]
s = 0.1 * np.sin(2 * np.pi * t) + cnse

fig, (ax0, ax1) = plt.subplots(2, 1)
ax0.plot(t, s)
ax1.psd(s, 512, 1 / dt)

plt.show()

Compare with equivalent MATLAB code

We can compare the previous code with the equivalent MATLAB code to accomplish the same thing:

dt = 0.01;
t = [0:dt:10];
nse = randn(size(t));
r = exp(-t/0.05);
cnse = conv(nse, r)*dt;
cnse = cnse(1:length(t));
s = 0.1*sin(2*pi*t) + cnse;

subplot(211)
plot(t, s)
subplot(212)
psd(s, 512, 1/dt)

Plot PSD with different amounts of padding

Next, we will plot the PSD with different amounts of zero padding. This uses the entire time series at once.

dt = np.pi / 100.
fs = 1. / dt
t = np.arange(0, 8, dt)
y = 10. * np.sin(2 * np.pi * 4 * t) + 5. * np.sin(2 * np.pi * 4.25 * t)
y = y + np.random.randn(*t.shape)

fig, axs = plt.subplot_mosaic([
    ['signal', 'signal', 'signal'],
    ['zero padding', 'block size', 'overlap'],
], layout='constrained')

axs['signal'].plot(t, y)
axs['signal'].set_xlabel('time [s]')
axs['signal'].set_ylabel('signal')

axs['zero padding'].psd(y, NFFT=len(t), pad_to=len(t), Fs=fs)
axs['zero padding'].psd(y, NFFT=len(t), pad_to=len(t) * 2, Fs=fs)
axs['zero padding'].psd(y, NFFT=len(t), pad_to=len(t) * 4, Fs=fs)

axs['block size'].psd(y, NFFT=len(t), pad_to=len(t), Fs=fs)
axs['block size'].psd(y, NFFT=len(t) // 2, pad_to=len(t), Fs=fs)
axs['block size'].psd(y, NFFT=len(t) // 4, pad_to=len(t), Fs=fs)
axs['block size'].set_ylabel('')

axs['overlap'].psd(y, NFFT=len(t) // 2, pad_to=len(t), noverlap=0, Fs=fs)
axs['overlap'].psd(y, NFFT=len(t) // 2, pad_to=len(t),
                   noverlap=int(0.025 * len(t)), Fs=fs)
axs['overlap'].psd(y, NFFT=len(t) // 2, pad_to=len(t),
                   noverlap=int(0.1 * len(t)), Fs=fs)
axs['overlap'].set_ylabel('')
axs['overlap'].set_title('overlap')

for title, ax in axs.items():
    if title == 'signal':
        continue

    ax.set_title(title)
    ax.sharex(axs['zero padding'])
    ax.sharey(axs['zero padding'])

plt.show()

Plot PSD of a complex signal

Finally, we will plot the PSD of a complex signal to demonstrate that complex PSD's work properly.

prng = np.random.RandomState(19680801)
fs = 1000
t = np.linspace(0, 0.3, 301)
A = np.array([2, 8]).reshape(-1, 1)
f = np.array([150, 140]).reshape(-1, 1)
xn = (A * np.exp(2j * np.pi * f * t)).sum(axis=0) + 5 * prng.randn(*t.shape)

fig, (ax0, ax1) = plt.subplots(ncols=2, layout='constrained')

yticks = np.arange(-50, 30, 10)
yrange = (yticks[0], yticks[-1])
xticks = np.arange(-500, 550, 200)

ax0.psd(xn, NFFT=301, Fs=fs, window=mlab.window_none, pad_to=1024,
        scale_by_freq=True)
ax0.set_title('Periodogram')
ax0.set_yticks(yticks)
ax0.set_xticks(xticks)
ax0.grid(True)
ax0.set_ylim(yrange)

ax1.psd(xn, NFFT=150, Fs=fs, window=mlab.window_none, pad_to=512, noverlap=75,
        scale_by_freq=True)
ax1.set_title('Welch')
ax1.set_xticks(xticks)
ax1.set_yticks(yticks)
ax1.set_ylabel('')
ax1.grid(True)
ax1.set_ylim(yrange)

plt.show()

Summary

In this tutorial, we have shown you how to plot the Power Spectral Density (PSD) using the Matplotlib library in Python. We began by plotting a basic PSD, then compared it to the equivalent MATLAB code. We then plotted the PSD with different amounts of zero padding, followed by plotting the PSD of a complex signal.