NumPy is a powerhouse for generating and manipulating signals—especially when you’re working with time-series data, audio, or simulations. Let’s break it down 🔊📈:

🧠 What Is NumPy?

NumPy (Numerical Python) is a core Python library for numerical computing. It’s widely used for:

• Creating arrays and matrices
• Performing mathematical operations
• Handling large datasets efficiently

When it comes to signal generation, NumPy helps you build synthetic signals like sine waves, square waves, and more.

🔊 How to Generate Signals with NumPy

Here’s a basic example of generating a sine wave:

import numpy as np
import matplotlib.pyplot as plt

# Parameters
fs = 1000          # Sampling frequency (Hz)
t = np.linspace(0, 1, fs, endpoint=False)  # Time vector (1 second)
f = 5              # Signal frequency (Hz)
amplitude = 1      # Signal amplitude

# Generate sine wave
signal = amplitude * np.sin(2 * np.pi * f * t)

# Plot it
plt.plot(t, signal)
plt.title("5 Hz Sine Wave")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.grid(True)
plt.show()

🧪 Other Signal Types

You can also generate:

• Square waves: np.sign(np.sin(...))
• Triangle waves: Use scipy.signal.sawtooth(..., width=0.5)
• Sawtooth waves: scipy.signal.sawtooth(...)
• Noise signals: np.random.normal(...) or np.random.rand(...)

These are great for testing filters, simulating systems, or training machine learning models.

🧬 Why It Matters

• Custom signal creation for simulations and testing
• Preprocessing for machine learning and AI
• Educational tools for understanding waveforms and frequency analysis

You can also combine NumPy with SciPy for advanced signal processing like filtering, convolution, and Fourier transforms.