Inverse FFT (ifft) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the inverse FFT function from scipy.

SciPy

from scipy.fft import [1]

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

Aifft

Brfft

Cfft

Dfftn

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fft' instead of 'ifft' will perform forward transform, not inverse.

Using 'rfft' is for real FFT, not inverse.

The inverse FFT function in scipy is called ifft. It converts frequency data back to time domain.

2fill in blank

medium

Complete the code to compute the inverse FFT of the array 'freq_data'.

SciPy

time_data = [1](freq_data)

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

Arfft

Bfft

Cifft

Dfftn

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fft' will not invert the transform.

Using 'rfft' or 'fftn' are incorrect for this purpose.

To get the time domain data from frequency domain, use ifft.

3fill in blank

hard

Fix the error in the code to correctly compute the inverse FFT of 'freq_array'.

SciPy

result = scipy.fft.[1](freq_array)

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

Arfft

Bifft

Cfft

Dfftn

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fft' instead of 'ifft' causes wrong output.

Using 'rfft' or 'fftn' are not the inverse FFT.

The correct function to compute inverse FFT is ifft from scipy.fft.

4fill in blank

hard

Fill both blanks to correctly prepare the frequency data with inverse shift before inverse FFT in the dictionary comprehension.

SciPy

inv_fft_dict = {k: ifft([1]([2])) for k, v in freq_dict.items()}

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

Aifftshift

Bv

Cfftshift

Dfreq_dict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fftshift' instead of 'ifftshift' applies the forward shift.

Using 'v' in wrong place or forgetting shift leads to misordered time data.

To center the zero-frequency component properly for inverse FFT, use ifftshift(v) on the frequency data v.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each frequency to the inverse FFT of its complex conjugate if the magnitude is greater than 1 and val is complex.

SciPy

inv_fft_map = {freq: ifft([1]) for freq, val in freq_vals.items() if abs(val) [2] 1 and [3]

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

Aval.conjugate()

B>

Cisinstance(val, complex)

Dval.real

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'val.real' instead of conjugate loses imaginary part.

Using '<' instead of '>' in magnitude check selects small values.

Incorrect type check like 'val == complex' fails.

The comprehension uses the conjugate of val inside ifft, checks if magnitude is greater than 1 with >, and verifies val is complex with isinstance(val, complex).