Spectrogram visualization in Signal Processing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library needed for plotting the spectrogram.

Signal Processing

import matplotlib.pyplot as [1]

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

Amatplot

Bmp

Cplt

Dplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect or uncommon aliases like 'mp' or 'matplot'.

Forgetting to import matplotlib.pyplot before plotting.

We use plt as the common alias for matplotlib.pyplot to plot graphs.

2fill in blank

medium

Complete the code to generate a spectrogram from the signal array using matplotlib.

Signal Processing

frequencies, times, Sxx = plt.specgram(signal, NFFT=[1], Fs=fs)

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

A128

B256

C1024

D512

Attempts:

3 left

💡 Hint

Common Mistakes

Using too small NFFT values like 128 which reduce frequency resolution.

Using values not a power of two which may cause errors.

The NFFT parameter defines the number of data points used in each block for the FFT. 512 is a common choice for good resolution.

3fill in blank

hard

Fix the error in the code to correctly label the x-axis as 'Time [sec]'.

Signal Processing

plt.xlabel([1])

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A'Time [sec]'

BTime [sec]

C'Time (seconds)'

DTime_sec

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the label string.

Using parentheses instead of square brackets for units.

The label text must be a string enclosed in quotes. 'Time [sec]' is the correct label.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each frequency to its power value if power is above 0.1.

Signal Processing

freq_power = {freq: power[1] for freq, power in zip(frequencies, map(max, Sxx)) if power [2] 0.1}

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A**2

B>

C>=

D*2

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of exponentiation for squaring.

Using >= instead of > which changes the filter condition.

We square the power value with **2 and filter frequencies where power is greater than 0.1 using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of frequencies and powers where power is above 0.05, and keys are rounded frequencies.

Signal Processing

filtered = { [1]: [2] for freq, power in freq_power.items() if power [3] 0.05}

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Around(freq)

Bpower

C>

Dfreq

Attempts:

3 left

💡 Hint

Common Mistakes

Not rounding the frequency keys.

Using <= or < instead of > for filtering.

We round the frequency keys with round(freq), keep the power values, and filter powers greater than 0.05.