FFT computation (fft) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the FFT function from scipy.

SciPy

from scipy.fft import [1]

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

Afft

Bifft

Cfftshift

Drfft

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the inverse FFT function ifft instead of fft.

Importing fftshift which is used for shifting zero frequency components.

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

2fill in blank

medium

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

SciPy

result = [1](signal)

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

Anp.fft

Bfft

Cscipy.fftshift

Difft

Attempts:

3 left

💡 Hint

Common Mistakes

Using ifft which computes the inverse FFT.

Using np.fft which is a module, not a function.

The fft function computes the FFT of the input array signal.

3fill in blank

hard

Fix the error in the code to correctly compute the FFT of the data array.

SciPy

from scipy.fft import fft

output = [1](data, n=128)

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

Afft

Bifft

Cfftshift

Drfft

Attempts:

3 left

💡 Hint

Common Mistakes

Using ifft instead of fft.

Using fftshift which only shifts the zero frequency component.

The fft function computes the FFT with specified length n=128.

4fill in blank

hard

Fill both blanks to create a dictionary of frequencies and their FFT magnitudes for values greater than 10.

SciPy

freq_magnitudes = {freq: abs(fft_result)[1] for freq, fft_result in zip(freqs, fft_values) if abs(fft_result) [2] 10}

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A** 2

B>

C>=

D* 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication * 2 instead of exponentiation ** 2.

Using >= instead of > in the condition.

The magnitude squared is calculated with abs(fft_result)**2, and we filter values greater than 10.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps frequency labels to FFT magnitudes for frequencies above 5 Hz.

SciPy

freq_dict = [1]: abs([2]) for [3], val in zip(freq_list, fft_vals) if [3] > 5}

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Af'Freq_{freq}'

Bval

Cfreq

Dfreq_list

Attempts:

3 left

💡 Hint

Common Mistakes

Using the list name instead of the loop variable in the condition.

Using raw frequency values as keys instead of formatted strings.

The dictionary keys are formatted frequency labels using f'Freq_{freq}'. The values are the magnitudes of FFT values abs(val). The loop variable is freq which is compared to 5.