How can I use NumPy's FFT for signal processing?

How can I use NumPy's FFT for signal processing?

You can use NumPy's FFT to compute the Fast Fourier Transform of a signal, which helps analyze its frequency components.

Can you show me a simple example of applying FFT on a sine wave?

Sure! First, create a sine wave using NumPy, then apply FFT to it. Here's a code snippet:

import numpy as np
import matplotlib.pyplot as plt

# Create a sample sine wave
fs = 1000  # Sampling frequency
t = np.arange(0, 1, 1/fs)
f = 5  # Frequency of the sine wave
signal = np.sin(2 * np.pi * f * t)

# Apply FFT
fft_result = np.fft.fft(signal)
frequencies = np.fft.fftfreq(len(fft_result), 1/fs)

# Plotting
plt.plot(frequencies, np.abs(fft_result))
plt.title('FFT of Sine Wave')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Magnitude')
plt.show()

What does the output of the FFT represent?

The output of the FFT represents the frequency components of the input signal, showing how much of each frequency is present in the original signal.

Are there any limitations to using FFT?

Yes, FFT assumes the input signal is periodic and can introduce artifacts if the signal is not properly windowed or if it has discontinuities.