FFT-based filtering in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute the FFT of the signal array.

SciPy

import numpy as np
from scipy.fft import fft

signal = np.array([1, 2, 3, 4, 5])
fft_result = [1](signal)

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

Afftshift

Bifft

Crfft

Dfft

Attempts:

3 left

💡 Hint

Common Mistakes

Using ifft instead of fft will compute the inverse transform.

fftshift rearranges FFT output but does not compute FFT.

rfft computes FFT for real inputs but is not the general FFT function.

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

2fill in blank

medium

Complete the code to create a frequency mask that keeps frequencies below 10 Hz.

SciPy

import numpy as np
sampling_rate = 50  # Hz
n = 100
freqs = np.fft.fftfreq(n, d=1/sampling_rate)
mask = np.abs(freqs) [1] 10

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

A<

B>

C<=

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using >= or > will select frequencies above 10 Hz, which is incorrect.

Using <= includes 10 Hz exactly, which might be acceptable but the question asks for below 10.

The mask should select frequencies less than 10 Hz, so the operator is <.

3fill in blank

hard

Fix the error in the code to apply a low-pass filter using FFT.

SciPy

import numpy as np
from scipy.fft import fft, ifft

signal = np.sin(2 * np.pi * 5 * np.linspace(0, 1, 100)) + np.sin(2 * np.pi * 20 * np.linspace(0, 1, 100))
fft_signal = fft(signal)
freqs = np.fft.fftfreq(len(signal), d=1/100)
mask = np.abs(freqs) [1] 10
filtered_fft = fft_signal * mask
filtered_signal = ifft(filtered_fft).real

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

A<

B>

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using > or >= will keep high frequencies instead of low frequencies.

Using <= includes 10 Hz exactly, which might be acceptable but the question expects strictly less.

The mask should be True for frequencies less than 10 Hz to keep them, so use <.

4fill in blank

hard

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

SciPy

words = ['data', 'science', 'is', 'fun']
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 as value instead of its length.

Using <= instead of > will select shorter words.

We want the length of each word, so use len(word). We only want words longer than 3 characters, so use >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of uppercase keys and values greater than 0.

SciPy

data = {'a': 1, 'b': -2, 'c': 3}
result = [1]: [2] for k, v in data.items() if v [3] 0

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase.

Filtering values less than or equal to zero.

Swapping keys and values.

The keys are converted to uppercase with k.upper(), values are v, and we filter values greater than 0 with >.