Windowing before FFT in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of applying a window function before performing an FFT?

Applying a window function reduces edge effects and spectral leakage by smoothing the signal edges before FFT.

beginner

Name a common window function used before FFT.

The Hamming window is a common window function used to reduce spectral leakage before FFT.

intermediate

What problem does spectral leakage cause in FFT analysis?

Spectral leakage causes energy from one frequency to spread into others, making it hard to identify true frequencies.

intermediate

How does the length of the window affect the FFT result?

Longer windows give better frequency resolution but worse time resolution; shorter windows do the opposite.

beginner

Show a simple Python code snippet using scipy to apply a Hamming window before FFT.

import numpy as np
from scipy.signal import windows
from scipy.fft import fft

signal = np.sin(2 * np.pi * 5 * np.linspace(0, 1, 100))
window = windows.hamming(len(signal))
windowed_signal = signal * window
fft_result = fft(windowed_signal)

Why do we use window functions before FFT?

A To reduce spectral leakage

B To increase signal amplitude

C To change the signal frequency

D To remove noise completely

Window functions smooth the signal edges to reduce spectral leakage during FFT.

Which of the following is a common window function?

A Median filter

B Gaussian blur

C Fourier transform

D Hamming

Hamming is a common window function used before FFT.

What does spectral leakage affect in FFT results?

A Signal length

B Frequency clarity

C Sampling rate

D Amplitude scaling

Spectral leakage spreads energy across frequencies, reducing clarity.

What happens if you do not apply a window before FFT?

A Signal becomes longer

B FFT runs faster

C More spectral leakage occurs

D Signal frequency changes

Without windowing, sharp edges cause spectral leakage.

In scipy, which function creates a Hamming window?

A scipy.signal.windows.hamming

B scipy.fft.hamming

C numpy.hamming

D scipy.signal.hann

scipy.signal.windows.hamming creates a Hamming window.

Explain why windowing is important before performing an FFT on a signal.

Think about what happens at the edges of the signal and how it affects frequency results.

Describe how to apply a window function to a signal using scipy before computing its FFT.

Focus on the steps from creating the window to computing the FFT.