How to plot signal in Matplotlib in Python?

Signal plotting in Matplotlib involves creating time-series visualizations that show how signal amplitude changes over time. This is essential for analyzing waveforms, noise patterns, and signal processing applications.

Basic Signal Plot

Let's create a simple sinusoidal signal with noise to demonstrate signal plotting ?

import matplotlib.pyplot as plt
import numpy as np

# Set figure size
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Set random seed for reproducible results
np.random.seed(0)

# Define sampling parameters
dt = 0.01  # sampling interval
Fs = 1 / dt  # sampling frequency
t = np.arange(0, 10, dt)

# Generate noise
nse = np.random.randn(len(t))
r = np.exp(-t / 0.05)
cnse = np.convolve(nse, r) * dt
cnse = cnse[:len(t)]

# Create signal: sine wave + noise
s = 0.1 * np.sin(4 * np.pi * t) + cnse

# Create the plot
fig, axs = plt.subplots()
axs.set_title("Signal")
axs.plot(t, s, color='C0')
axs.set_xlabel("Time")
axs.set_ylabel("Amplitude")

plt.show()

Multiple Signal Comparison

You can plot multiple signals on the same axes for comparison ?

import matplotlib.pyplot as plt
import numpy as np

# Time parameters
t = np.linspace(0, 2 * np.pi, 100)

# Different signals
signal1 = np.sin(t)
signal2 = np.sin(2 * t)
signal3 = np.sin(t) + 0.5 * np.sin(3 * t)

# Plot multiple signals
plt.figure(figsize=(10, 6))
plt.plot(t, signal1, label='sin(t)', linewidth=2)
plt.plot(t, signal2, label='sin(2t)', linewidth=2)
plt.plot(t, signal3, label='sin(t) + 0.5*sin(3t)', linewidth=2)

plt.title('Multiple Signal Comparison')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True, alpha=0.3)

plt.show()

Signal Processing Steps

Key Parameters

• Sampling interval (dt) − Time between consecutive samples
• Sampling frequency (Fs) − Number of samples per second
• Time array (t) − Sequence of time points for the signal
• Signal amplitude − Magnitude of the waveform at each time point

Conclusion

Signal plotting in Matplotlib requires defining time parameters, generating signal data, and using plot() with proper labels. The key is setting appropriate sampling rates and adding meaningful annotations for clear visualization.