FFT with np.fft module in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the FFT function from numpy's fft module.

NumPy

from numpy.fft import [1]

signal = [1, 2, 3, 4]
fft_result = [1](signal)

Drag options to blanks, or click blank then click option'

Aifft

Bfftshift

Cfft

Drfft

Attempts:

3 left

💡 Hint

Common Mistakes

Using ifft which computes the inverse FFT.

Using fftshift which shifts zero frequency component.

Using rfft which is for real input FFT.

The fft function computes the Fast Fourier Transform of a signal.

2fill in blank

medium

Complete the code to compute the inverse FFT of a frequency domain signal.

NumPy

import numpy as np
freq_signal = np.array([10+0j, -2+2j, -2+0j, -2-2j])
time_signal = np.[1](freq_signal)

Drag options to blanks, or click blank then click option'

Aifft

Brfft

Cfft

Dirfft

Attempts:

3 left

💡 Hint

Common Mistakes

Using fft which computes forward FFT.

Using rfft which is for real input FFT.

Using irfft which is inverse FFT for real input only.

The ifft function computes the inverse FFT, converting frequency domain back to time domain.

3fill in blank

hard

Fix the error in the code to compute the FFT of a signal using numpy.

NumPy

import numpy as np
signal = [1, 2, 3, 4]
fft_result = np.fft.[1](signal)

Drag options to blanks, or click blank then click option'

Afftshift

Bfft

Cifft

Drfft

Attempts:

3 left

💡 Hint

Common Mistakes

Using ifft instead of fft.

Using fftshift which only shifts the zero frequency component.

Using rfft which is for real input FFT but not the general FFT.

The correct function to compute FFT in numpy's fft module is fft.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Using the wrong comparison operator like <=.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words mapped to their lengths for words longer than 2 characters.

NumPy

words = ['fft', 'np', 'data', 'ai']
result = { [1]: [2] for word in words if len(word) [3] 2 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase.

Using the wrong comparison operator like <.

The dictionary comprehension maps uppercase words to their lengths if the word length is greater than 2.